fingerprint image classification and retrieval using...

FINGERPRINT IMAGE CLASSIFICATION AND

RETRIEVAL USING STATISTICAL METHODS

A Thesis submitted to Gujarat Technological University

for the Award of

Doctor of Philosophy

in

Computer IT Engineering

by

Sudhir P Vegad

119997107012

under the supervision of

Dr. Tanmay Pawar

GUJARAT TECHNOLOGICAL UNIVERSITY AHMEDABAD

August – 2018

ii

© Sudhir P Vegad

iii

DECLARATION

I declare that the thesis entitled Fingerprint Image Classification and Retrieval using

Statistical Methods submitted by me for the degree of Doctor of Philosophy is the record

of research work carried out by me during the period from October 2011 to August 2018

under the supervision of Dr. Tanmay Pawar and this has not formed the basis for the award

of any degree, diploma, associateship, fellowship, titles in this or any other University or

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noticed in the thesis.

Signature of the Research Scholar: ......................... Date: ….………

Name of Research Scholar: Sudhir P Vegad

Place : Vallabh Vidyanagar, Anand

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I certify that the work incorporated in the thesis Fingerprint Image Classification and

Retrieval using Statistical Methods submitted by Sudhir P Vegad was carried out by the

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Place: Vallabh Vidyanagar, Anand

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The viva-voce of the PhD Thesis submitted by Shri/Smt./Kum. Sudhir P Vegad (Enrollment

No. 119997107012) entitled Fingerprint Image Classification and Retrieval using

Statistical Methods was conducted on …………………….………… (day and date) at

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ABSTRACT

Human fingerprints are popular biometrics due to its characteristics like universality,

uniqueness, permanence, collectability and acceptability. Fingerprints are rich in details

which are known as minutiae, which can be used as identification marks to uniquely

identify the fingerprint for various purposes. There are four basic patterns of fingerprint

ridges; arch, left loop, right loop and whorl.

Fingerprint classification is an important indexing scheme to narrow down the search of

fingerprint database for efficient large-scale retrieval. It is still a challenging problem due

to the intrinsic class ambiguity and the difficulty for poor quality fingerprints. Fingerprint

retrieval is a pre-requisite step for many applications like recognition and registration.

Feature extraction plays a very important role in retrieval process. The work presented in

this research has two phases; classification and retrieval. The first phase classifies the

fingerprint image and second phase performs retrieval from the classified class in phase

one. In classification phase, two classification methods are used. In the first method, scale

and rotation invariant GLCM and LBP features are concatenated to enhance the local

information from the fingerprint images for powerful representation. In second method,

SURF and SIFT features are concatenated to strongly represent the class-uniqueness of

fingerprint images. Classification is carried out by applying these features in classification

models, kNN, SVM and BoF. The retrieval phase matches the fingerprint image features

using distance matching methods to retrieve similar images from the class declared from

classification phase. The experimental results and comparisons on FVC2000, FVC2002,

FVC 2004 and NIST - 4 databases have shown the effectiveness and superiority of the

proposed method for fingerprint classification and retrieval.

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Acknowledgement

It is a pleasant aspect that I have now the opportunity to express my gratitude for many

people who accompanied and supported me during the journey of my work. Firstly, I offer

my adoration to almighty who created me, gave me the strength and courage to complete

my research work. I would like to express my deep gratitude to my supervisor Dr. Tanmay

Pawar, Professor and Head, Department of Electronics Engineering, BVM Engineering

College, Vallabh Vidyanagar, for his guidance and whole hearted cooperation. His helping

nature, valuable moral support and appreciation have inspired me and will continue to

inspire for the rest of my career.

I would also like to extend my sincere gratitude to my Foreign Co-Supervisor, Dr. Arun

Somani, Associate Dean for Research, IOWA State University, IA, for his cooperation,

help and motivation throughout the research work. I would like to thank my doctoral

progress committee: Dr. Narendra M Patel, Associate Professor, BVM Engineering

College and Dr. Shanmuganathan Raman, Assistant Professor, Electrical Engineering

Department, IIT, Gandhinagar for their insightful comments and encouragement during

entire period of research work.

It is an honour for me to express my deep sense of gratitude to Dr. C. L. Patel Sir, former

Chairman, Charutar Vidya Mandal (CVM) for giving permission to pursue in-service

Ph.D. I am extremely thankful to Dr. R. K. Jain Principal, ADIT for his kind support,

continuous inspiration and providing necessary resources during the research work. I take

this opportunity to thank Dr. Narendra Chauhan, Head, Information Technology

Department, ADIT for providing me mental support and inspiration. It is my pleasure to

acknowledge the help and suggestions from Mr. Prashant Italiya, alumni of ADIT.

I also would like to thank my students for helping me in carrying out this work. I also

thank teaching and non-teaching staff of Department of Information Technology and

Computer Engineering Department, ADIT who participated and helped me in creating

fingerprint image dataset.

I am short of words for my family members being grateful to them for all the sacrifices

they have made for me. I owe deepest gratitude to my parents, my sister and in-laws for

their mental support. My heartiest thank to my wife and true inspiration, Zankhana Shah,

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for her care and all the sacrifices. This work would not have been completed without her

unconditional love and support during entire period of my work. I am short of words to

express my loving gratitude to my adorable son Dirgh, whose innocent smile and love has

given me mental support during the entire work.

Sudhir P Vegad

xiv

Table of Content

1 Introduction

1.1 Overview 1

1.1.1 Performance Metrics 4

1.2 Fingerprints 6

1.2.1 Principles of Fingerprint 8

1.2.2 Applications 8

1.2.3 Types of Fingerprints 9

1.2.4 Classification of Finger Prints 10

1.2.5 Fingerprint Detection Methods 14

1.2.6 Fingerprint Retrieval 17

1.3 Motivation 21

1.4 Research objectives 22

1.5 Research Contribution 22

1.6 Organization of the thesis 23

2 Theoretical Background and Literature Survey

2.1 Introduction 24

2.2 Biometric Security 24

2.3 Fingerprint Recognition System 26

2.4 Fingerprint Acquisition Methods 28

2.5 Fingerprint Pre-processing 29

2.6 Fingerprint Retrieval 33

3 Classification using LBP and GLCM Features

3.1 Introduction 42

3.1.1 Fingerprint Classification 42

3.1.2 Feature Extraction 43

3.1.3 Feature extraction methods 44

3.2 Methodology 48

3.2.1 Gray level co-occurrence matrix (GLCM) 48

3.2.2 Local Binary Patterns (LBP) 51

3.2.3 Fingerprint Classification using Support Vector Machine (SVM) 52

3.2.4 Fingerprint Classification using kNN 53

3.2.5 Combining GLCM and LBP 55

3.3 Experimental results 55

3.3.1 Classification Results using SVM Classifier 56

3.3.2 Classification Results with kNN Classifier 60

3.4 Concluding remarks 64

4 Classification using Bag of Features (BoF)

4.1 Introduction 65

4.2 Methodology 66

4.2.1 Feature Extraction using Speeded Up Robust Features (SURF) 66

4.2.2 Fingerprint Classification using Bag of Features (BoF) 68

4.2.3 Classification model of SURF Features with BoF 71


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5 Retrieval using Scale and Rotation Invariant Robust Features

5.1 Introduction 78

5.2 Methodology 79

5.2.1 Feature Extraction using Scale Invariant Feature Transform

(SIFT)

80

5.2.2 Feature Extraction using fusion method of SURF and SIFT 84

5.2.3 Retrieval using Bag of Features 84



6 Conclusion and Further Directions

6.1 Conclusion 95

6.2 Further Directions 96

List of References 97

List of Publications 111

xvi

List of Abbreviations

AFIT Advance Fingerprint Identification Technology

BoF Bag of Features

EER Equal Error Rate

FAR False Accept Rate

FMR False Match Rate

FRR False Rejection Rate

GLCM Gray Level Co-occurrence Matrix

JPEG Joint Photographic Experts Group

kNN K-Nearest Neighbour

LBP Local Binary Pattern

LDA Linear Discriminant Analysis

PCA Principal Component Analysis

PCASYS Pattern-Level Classification Automation System

ROC Receiver Operating Characteristics

SIFT Scale Invariant Feature Transformation

SURF Speeded-Up Robust Feature

SVM Support Vector Machine

UIDAI Unique Identification Authority of India

xvii

List of Figures

1.1 Parameters of Biometrics 3

1.2 Confusion Matrix for calculating hit rate for a classification model 4

1.3 A basic ROC graph showing five discrete classifiers 5

1.4 Fingerprint Classes and Subclasses, a) Arch, b) Loop and c) Whorl 11

1.5 Block Diagram of Fingerprint Retrieval system 19

2.1 Typical Fingerprint-based Biometric System 26

2.2 Typical flow of fingerprint image pre-processing 30

3.1 Taxonomy of features used for fingerprint classification 44

3.2 Co-occurrence matrix directions for extracting texture features 49

3.3 GLCM construction based on a test image along four possible directions

(0º, 45º, 90º and 135º) with distance d = 1

50

3.4 Example of LBP operator 51

3.5 Process of generating LBP features 52

3.6 Hyper-plane for classifying samples in SVM 53

3.7 Example for kNN 54

3.8 Classification flow using Fusion features with SVM / KNN 55

3.9 Sample images from the dataset used; a) NIST – 4, b) Captured 56

3.10 Confusion matrix of Testcase1 using SVM Classifier 57





3.15 Analysis of Results of various Testcases (with SVM Classifier) 59

3.16 Analysis of Results of various classes (with SVM Classifier) 60

3.17 Confusion matrix of Testcase1 using kNN Classifier 61





3.22 Analysis of Results of various Testcases (with kNN Classifier) 63

3.23 Analysis of Results of various classes (with kNN Classifier) 64

4.1 Haar wavelet for gradient calculation in (a) x-direction and (b) y-direction 67

xviii

4.2 Example for Bag of Feature Classification 69

4.3 Histogram representation for BoF 70

4.4 Classification flow using SURF features with BoF 71

4.5 Sample images from the dataset used; a) NIST – 4, b) Captured 72

4.6 Confusion matrix of Testcase1 using BoF Classifier 73





4.11 Analysis of Results of various Testcases (with BoF Classifier) 75

4.12 Analysis of Results of various classes (with BoF Classifier) 76

5.1 Keypoint localization at different scales 82

5.2 Orientation Histogram 83

5.3 Computation of Keypoint Descriptors 83

5.4 Image Retrieval Based on Bag of Words 86

5.5 Example for k-Means Clustering with 4 clusters 89

5.6 Retrieval Flow 90

5.7 Sample images from the dataset used; a) FVC2002, b) FVC2004 and c)

Captured

92

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List of Tables

3.1 Comparative Summary of feature extraction and classification methods 47

4.1 Comparison of average Testcase accuracy and class accuracy of the

approaches used for classification

76

5.1 Retrieval Result analysis of FVC dataset images without performing

classification phase

93

5.2 Retrieval Result analysis of FVC dataset images after performing


93

5.3 Retrieval Result analysis of Captured Image dataset images without

performing classification phase

93

5.4 Retrieval Result analysis of Captured Image dataset images after performing


94

Introduction

1

CHAPTER – 1

Introduction

1.1 Overview

Security concerns are the real worry in this day and age and are proceeding to develop in

intensity and many-sided quality. Security is a perspective that is given best need by

associations, educational organizations, political and government in personality and

extortion issues. In light of the new dangers, associations need to execute or refresh a staff

security program to avoid unapproved access to control systems and basic data [1]. As per

the Personal Security Guidelines, productive techniques should be created for faculty

security and control system trustworthiness. Associations ought to work safely with the

goal that they don't constitute an unsuitable security chance that could affect the wellbeing

of other faculty or the general population.

Reliable authentication of individuals has for some time been a fascinating objective, and

is ending up more imperative as the requirement for security develops. Innovations called

biometrics can robotize the distinguishing proof of individuals by at least one of their

particular physical or behavioural attributes. They are design acknowledgment

frameworks, which make an individual distinguishing proof by deciding the credibility of

the particular trademark controlled by the client [2]. Biometrics has come to possess an

undeniably critical part in human distinguishing proof due fundamentally to their

comprehensiveness and uniqueness. Because of this advancement, another type of systems

and strategies for client personality acknowledgment and check has seemed in light of the

biometric highlights that are remarkable to every individual [3].

Biometrics is an exploration of confirming and building up the character of a person

through physiological highlights or behavioural characteristics. Biometric innovations

fluctuate in complexity, abilities and execution; however all offer a few normal

components. Biometric recognizable proof systems are basically design acknowledgment

frameworks. They utilize procurement gadgets, for example, cameras and examining

Introduction

2

gadgets to catch images, chronicles or estimations of a person's qualities and PC equipment

and programming to remove, encode, store and look at these attributes [4]. As the

procedure is computerized, biometric basic leadership is by and large quick, and much of

the time, takes just a couple of moments continuously.

It is utilized as a type of personality get to administration and access control. It is likewise

used to distinguish people in bunches that are under observation. Utilize biometrics go

back finished a thousand years. In East Asia, potters set their fingerprints on their products

as an early type of brand character. In Egypt's Nile Valley, dealers were formally

recognized in light of physical attributes, for example, tallness, eye shading, and

appearance [5]. This data distinguished trusted brokers whom dealers had effectively

executed business before. The Old Testament likewise gives early cases of voice

acknowledgment and biometric spoofing. Since 1970, biometrics is started to be used to

identify and authenticate a person using a set of recognizable and verifiable data unique

and specific to that person.

One of the primary business applications was utilized as a part of 1972 when a Wall Street

organization, Shearson Hamil, introduced Identimat, a finger-estimation gadget that filled

in as a period keeping and checking application. Since this organization, biometrics has

enhanced immensely in convenience and assorted variety of utilizations. The headway of

biometrics has been driven by the expanded figuring power at bring down costs, better

calculations, and less expensive stockpiling systems accessible today [6].

There are two modes in which biometric frameworks can be utilized. They are

confirmation and ID. 'Confirmation', likewise called 'validation', is utilized to check a

man's personality, that is, to verify that people are who they say they are. 'ID' is utilized to

build up a man's personality, that is, to figure out who a man is. Despite the fact that

biometric advances measure diverse qualities in considerably extraordinary ways, all

biometric frameworks include comparative procedures that can be isolated into two

particular stages [7]: Enrolment & Verification or Identification.

Biometric characteristics can be divided in two main classes:

Physiological characteristics

Behavioural characteristics

Introduction

3

Physiological attributes are identified with the state of the body and incorporate

fingerprint, face, DNA, ear, palm print, hand geometry, iris, retina and smell/fragrance.

Behavioural qualities are identified with the conduct of a man like mark, keystroke

progression, stride and voice. A few analysts have instituted the term behaviometrics to

depict the last class of biometrics.

Voice is additionally a physiological characteristic on the grounds that each individual has

an alternate vocal tract, however voice acknowledgment is fundamentally in light of the

investigation of the way a man talks, generally named behavioural. It is conceivable to

comprehend if a human trademark can be utilized for biometrics as far as the

accompanying parameters:

FIGURE 1.1: Parameters of Biometrics

Acceptability: Degree of approval of a technology.

Circumvention: Ease of use of a substitute.

Collectability: Ease of acquisition for measurement.

Performance: Accuracy, speed, and robustness of technology used.

Introduction

4

Permanence: Measures how well a biometric resists aging and other variance over time.

Uniqueness: Indicates how well the biometric separates individuals from another.

Universality: Each person should have the characteristic.

By utilizing biometrics it is conceivable to set up a character in light of your identity, as

opposed to by what you have, for example, an ID card, or what you recall, for example, a

watchword. In a few applications, biometrics might be utilized to supplement ID cards and

passwords in this way giving an extra level of security. Such a plan is frequently called a

double factor verification conspire [8].

The vast majority of the works found in the writing about biometric based verification,

utilizes parameters like iris, voice, fingerprints, and confronts qualities and others. Out of

these, for quite a while, the fingerprints have been a standout amongst the most broadly

utilized and acknowledged biometric. This is confirmed by the Chinese who have utilized

fingerprints to sign archives for more than 1000 years.

1.1.1 Performance Metrics

A classification model is a mapping from instances to predicted classes [187]. As shown in

Fig. 1.2 each instance I is mapped with one element of the set {p,n} of positive or negative

class labels. To distinguish between actual class and predicted class {Y,N} labels are used

for the predictions produced by the classification model.

FIGURE 1.2: Confusion Matrix for calculating hit rate for a classification model

Hypothesized

Class

Y

N

True Class

p n

True

Positives

False

Positive

False

Negatives

True

Negatives

P N Column Totals:

Introduction

5

Given a classifier and an instance, there are four possible outcomes. If the instance is

positive and it is classified as positive, it is counted as a true positive; if it is classified as

negative, it is counted as a false negative. If the instance is negative and it is classified as

negative, it is counted as a true negative; if it is classified as positive, it is counted as a

false positive. Given a classifier and a set of instances (the test set), a two-by-two

confusion matrix (also called a contingency table) can be constructed representing the

dispositions of the set of instances. This matrix forms the basis for many common metrics.

The true positive rate and false positive rates can be calculated by the following equations,

True Positive (TP) Rate = Positives Correctly Classified / Total Positives (1.1)

False Positive (FP) Rate = Positives Correctly Classified / Total Negatives (1.2)

TP rate and FP rate are plotted on receiver operating characteristics (ROC) graph on Y axis

and X axis respectively. Each discrete classifier produces an (FP rate, TP rate) pair

corresponding to a single point in ROC space. Fig. 1.3 shows five such pair labels, A

through E. One point in ROC space is better than another if it is to the northwest (TP rate is

higher, FP rate is lower, or both). D‘s performance is perfect in the case shown in Fig. 1.3.

FIGURE 1.3: A basic ROC graph showing five discrete classifiers

In case of fingerprint classification, if the fingerprint actually belongs to a class but it is not

identified by the classifiers, it falls in to false negative. As fingerprint classification is used

as a prior step for many fingerprint applications like retrieval, recognition, etc., false

A.

C.

D. B.

0.2 0.4 0.6 0.8 1.0 0

0.2

0.4

0.6

0.8

1.0

0

E.

Tru

e P

osi

tiv

e R

ate

False Positive Rate

Introduction

6

negatives are more problematic as it will never give correct result in retrieval or

recognition stage.

There are few other parameters or measurements for biometric framework verifications.

False Accept Rate or False Match Rate (FAR or FMR): The likelihood that the

framework inaccurately coordinates the information example to a non-coordinating format

in the database. It measures the percentage of invalid data sources which are erroneously

acknowledged.

False Reject Rate or False Non-Match Rate (FRR or FNMR): The likelihood that the

framework neglects to distinguish a match between the info design and a coordinating

layout in the database. It quantifies the percentage of substantial data sources which are

inaccurately dismissed.

Equal Error Rate or Crossover Error Rate (EER or CER): The rate at which both

FAR and FRR are equivalent. The estimation of the EER can be effortlessly gotten from

the ROC bend. The EER is a snappy method to contrast the precision of gadgets and

diverse ROC bends. When all is said in done, the gadget with the least EER is generally

precise. Acquired from the ROC plot by taking the point where FAR and FRR have a

similar esteem.

1.2 Fingerprints

Fingerprint, palm print, hand vein, hand/finger geometry, iris, retina, confront, ear, scent or

the DNA data of an individual come in physiological classification. A unique mark takes a

gander at the examples found on a fingertip. There is an assortment of ways to deal with

fingerprint confirmation. Some imitate the customary police strategy for coordinating

particulars; others utilize straight example coordinating gadgets; and still others are more

novel, including things like moiré periphery designs and ultrasonic. Some confirmation

methodologies can identify when a live finger is introduced; some can't [9].

Fingerprint recognition is outstanding amongst other known and most broadly utilized

biometric innovations and is financially accessible. A fingerprint based biometric is a

framework that perceives a man by deciding the realness of his/her finger impression.

Introduction

7

The fingerprint properties of a man are exceptionally precise and are interesting to a

person. Verification frameworks in light of unique finger impression have demonstrated to

create low false acknowledgment rate and false dismissal rate, alongside different

favourable circumstances like simple and minimal effort execution methodology [10].

Additionally, the fingerprint impression regularly stays unaltered from birth until death.

Aside from being interesting and constant, fingerprints can be accumulated in a non-

intrusive way with no reactions. The present unique finger impression coordinating

innovation is very develop for coordinating full prints, while coordinating fractional unique

mark still needs part of change. The requirement for incomplete fingerprint

acknowledgment frameworks are expanding in both non-military personnel and scientific

applications [11].

In non-military personnel, the creation of little hand-held gadgets, for example, cell

phones, scaled down unique mark sensors deliver just little parts of fingerprints. Scaling

down of fingerprint sensors has prompted little detecting territories of 0.42" x 0.42". Be

that as it may, as per FBI determinations, a normal of 1.0" x 1.0" finger print impression

territory is expected to gather particulars focuses precisely [12]. The decreased fingerprint

estimate diminishes the precision of acknowledgment while utilizing fractional fingerprints

and looks into are still on-going to enhance this. The most alluring properties of high

exactness, low blunder rate and greatest speed are yet to be accomplished with Automatic

Partial Fingerprint Recognition System (APFS).

Fingerprint recognition is one of the most established types of biometrics that has been

ended up being profoundly solid. The finger print impression of every individual is

particular and can have a place with just a single individual and does not change amid their

lifetime. Besides, the equipment and programming for unique finger impression

acknowledgment are progressively getting to be noticeably less expensive [13]. Utilizing

fingerprints for verification is an all-around acknowledged arrangement and a greater part

of the populace has intelligible fingerprints. This is more than the quantity of individuals

who have travel papers, permit and Identity cards. It has exceptionally unmistakable

highlights and is a standout amongst the most precise types of biometrics accessible. And

in addition to that is due to the following convenience the fingerprints, as biometrics, are

widely accepted for identity authentication [14]:

Introduction

8

i) The obtaining procedure is non-noisy, requiring no preparation.

ii) It is the most conservative biometric validation strategy.

iii) Small storage room is required for the biometric format, diminishing the extent of the

database memory required.

1.2.1 Principles of Fingerprint

The essential standards of fingerprint innovation were logically settled by Sir Francis

Galton, a British Scientist viewed as the Father of Fingerprint science and are given

underneath [15]:

Permanency of Persistency: Fingerprints never show signs of change from birth to death

till decay sets in. The edges on the grating skin of fingers are produced with settled and

changeless characters in one and half to two months of gestation and are full-fledged in

180 days. These arrangements extend as per the development of the body and in this way

stay lasting till the skin perishes or is harmed.

Individuality or variety: No two fingerprints will be indistinguishable unless they are

taken from a similar finger of a similar individual. Fingerprints taken from various digits of

a similar individual will likewise not be indistinguishable. In this manner, fingerprints are

God's own particular seal to distinguish a person.

Immutability of fingerprints: As said already, there are two layers in human skin in

particular, epidermis and dermis. Straight forward wounds, age, development, peeling of

skin, warts, wrinkles, consumes, and skin maladies will influence the edges in the

epidermis briefly. Be that as it may, after some time, the influenced edges recover their

unique development of examples and edge qualities. In any case, skin wounds that scar

influence the dermis are perpetual and consequently change the development of examples

and edges.

Consequently, it can be comprehended that fingerprints are trustworthy for the duration of

human life and in this way, are most dependable, honest, unchangeable and steadfast in

setting up the character of a man.

Introduction

9

1.2.2 Applications

Fingerprint biometric has been utilized as a part of various applications including

passageway control and entryway bolt applications, savvy cards, vehicle start control

frameworks and fingerprint controlled access. Fingerprint impression is a standout

amongst the most incessant and most solid sources utilized as a part of criminology to

recognize hoodlums and deceitful people. It is additionally utilized as a part of the

recognizable proof of unidentified body where fingerprint impression records are

contrasted and existing databases [16]. They are progressively utilized as a part of

numerous imperative administration situated ventures like managing an account,

authorizing and identification, where use of fingerprint diminishes the frequency of

fabrication, pantomime and fakes as it were. They are utilized as a part of property and

common situations where the use of authentic records of the enrolment office and on

archives explains basic cases [17].

As of late, fingerprints caught from infants are utilized as a part of doctor's facilities to stay

away from oversight of compatibility by utilizing a solitary care to store the print of

mother and tyke together. It is additionally utilized by seniority beneficiaries and numerous

regular administrations like railroad, power board, where impermanent workers are

fingerprinted to maintain a strategic distance from assemble part cheats [18]. These days,

fingerprints are utilized to open PCs. Programming prepared bolting framework is

accommodated solid rooms of banks and even entryways of houses along these lines

keeping away from unapproved passage. Therefore, with its wide utilization, fingerprint

impression acknowledgment frameworks are of appeal.

1.2.3 Types of Fingerprints

From long time past days, three sorts of fingerprints can be gained, to be specific, exemplar

prints, patent prints and plastic prints [19].

Exemplar prints, or known prints, is the name given to fingerprints intentionally

gathered from a subject, regardless of whether for motivations behind enlistment in a

framework or when in custody for a presumed criminal offense. These prints can be

gathered utilizing Live Scan or by utilizing ink on paper cards.

Introduction

10

Patent prints are chance contact edge impressions which are clear to the human eye and

which have been caused by the exchange of outside material from a finger onto a surface.

Some undeniable illustrations would be impressions from flour and wet earth. Since they

are now obvious and have no need of upgrade they are by and large captured instead of

being lifted in how idle prints are. Patent prints can be left on a surface by materials, for

example, ink, earth, or blood.

A plastic prints is a grinding edge impression left in a material that holds the state of the

edge detail. Albeit not very many lawbreakers would be sufficiently indiscreet to leave

their prints in a piece of wet dirt, this would make an immaculate plastic print. Ordinarily

experienced illustrations are softened light wax; putty expelled from the border of window

sheets and thick oil stores on auto parts. Such prints are now unmistakable and require no

upgrade. Independent of these three kinds, the fingerprints in this way gathered can fall

into three classes, in particular, Rolled/full, Plain/level and Latent or halfway.

Moved fingerprint images are acquired by rolling a finger from one side to the next ("nail-

to-nail") with a specific end goal to catch all the edge points of interest of a finger. Plain

impressions are those in which the finger is pushed down on a level surface yet not rolled.

Rolled and plain impressions are acquired either by filtering the inked impact on paper or

by utilizing live-check gadgets. Since rolled and plain fingerprints are procured in a gone

to mode, they are normally of good quality and are rich in data content.

The fingers have tiny outlets for the expulsion of sweat and these outlets pick up salts, oils

and tiny particles of dirt along the way. These particles can then be passed to hard surfaces

and generate fingerprints that are not visible to the naked eye. Such fingerprints are known

as Latent prints. Latent are visualised using magnesium powder which is gently brushed

over these hard and shiny surfaces in order to illuminate them. Once this is done the prints

can be photographed or 'lifted' using a variety of different tapes without delay as some

prints can fade quickly [168].

1.2.4 Classification of Finger Prints

Dr. Jan. E. Purkinje, Professor of Physiology in the University of Breslau has characterized

all fingerprints into eight sorts; the transverse bends, the central longitudinal strip, the

oblique strip, the almond whorl, the spiral whorl, the ellipse, the circle and the double

whorl [20]. Edward Richard Henry [181] adjusted Dr. Jan. E. Purkinje's eight sorts

Introduction

11

characterized them into four primary classes as indicated by the level of their circulation in

the entire populace of the world, these are Loop (65-67)%, Whorl (25)%, Arch (6-7)% and

Composite or Accidental or Chance (3-4)%. This technique is known as "Henry Galton

strategy" or "Henry strategy", is name being got from its originators Sir Francis Galton and

Sir Edward Richard Henry. This Henry arrangement of characterization is most effective

and is in relatively all inclusive utilize.

Fig. 1.4 shows the subclasses of each primary class, Arch, Loop and Whorl.

FIGURE 1.4: Fingerprint Classes and Subclasses, a) Arch, b) Loop and c) Whorl

Arch: These are described by a slight ascent in the edges which enter on the finger‘s one

side and also exit on the contrary side. The arches are of two subtypes- plain arch and

tented arch. Neither the plain arch nor the tented arch has a delta formation [23].

Plain Arch: Plain curve is the most straightforward of all the fingerprint designs, and is

effectively recognized. In plain curves the edges enter on one side of the impression and

stream or tend to stream out on the opposite agree with an ascent or wave in the middle.

There might be various edge arrangements, for example, finishing edges, bifurcations,

dabs and islands engaged with this kind of example, yet they each of the have a

tendency to take after the general edge shape, i.e. they enter on one side, make an ascent

or a wave in the inside, and stream or tend to stream out on the opposite side.

Arch

Tented

Arch

Plain

Arch

Exceptional

Arch

Loop

Radial

Loop

Ulnar

Loop

Whorl

Central

Pocket

Loop

Double

Loop

Twinned

Loop

(c)

Accidental /

Composite

Introduction

12

Tented Arch: Tented Arch is the one which the vast majority of the edges enter upon

one side of the impression and stream or tend to stream out upon the opposite side as in

the plane curve compose; in any case, the edge or edges at the inside don't. Tented

curves and a few types of circles are regularly befuddled. It ought to be recalled that the

negligible merging of two edges does not frame a re-curve, without which there can be

no circle arrangement.

Exceptional Arch: Their structure is more shifted than that of customary rose curves,

and may incorporate a portion of the attributes of a circle, in spite of the fact that

insufficient to give them a circle grouping.

Loop: Regarding fingerprints, and in addition in the general use of the word circle, there

can't be a circle unless there is a re-bend or recovery of at least one of the edges alongside

the other pre-essentials. An example must have a few necessities previously it might be

legitimately named a circle [21]. Nonetheless, this kind of example is the most well-known

and constitutes around sixty to sixty five percentage of all prints. A loop is that kind of

fingerprint design in which at least one of the edges enter on either side of the impression,

re-bend, touch or pass a non-existent line attracted from the delta to center and ends or has

a tendency to end on or towards a similar side of the impression from where edge or edges

enter. A circle has one and just a single delta.

As showed by their arranging and the circulation of the edges, loops are partitioned into

two fundamental writes. They are radial loop and ulnar loop.

Radial Loop: Radial Loop is charged in light of the fact that the edges stream or end

toward the lower arm‘s sweep bone. On the off chance that there ought to be an event of

the left hand fingers, the edges slant towards the right side and in the right hand fingers,

the slant is towards the left side.

Ulnar Loop: Ulnar Loop is alleged in light of the fact that the edges stream or end

toward the lower arm‘s ulnar bone, if there ought to be an event of the left hand fingers,

the edges slant towards the left side and for the right hand fingers the slanting of the

edges is towards the right side.

Whorl: A whorl is depicted by an indirect case having no less than one edges turn around

the middle making an aggregate circle. The whorl is that kind of case in which no under

two deltas are accessible with a re-twist before each. Whorl compose outlines occur in

Introduction

13

around 30% of all fingerprints [22]. Note that this is an especially wide definition. This

case, regardless, may be subdivided for extension reason in tremendous social occasions

where whorls are commanding. Regardless of the way that this increase may be used,

extensive varieties of whorls are amassed under the general name of whorl and are created

by letter 'W'. The subdivision of whorl configuration is according to the accompanying.

Double Loop: Twofold Loops are composite examples and comprises of two discrete

and particular arrangements of shoulders and two deltas. The two circles might be

associated with each other by an attaching edge gave that it doesn't adjoin at right points

between the shoulders of the arrangement. It isn't fundamental that the two sides of the

circle be of equivalent length, nor that two circles be of a similar size. The twofold

circles are of two kinds - parallel pocket circle and twinned circle. The refinement

between twinned circle and sidelong circle was made by Henry.

Central Pocket Loop: In this illustration one circle fills in as a side pocket to the

following circle, in the lateral pocket loop. This pocket is shaped by the descending

twisting on one of the sides of the edges of the other circle before they recurve. The

edges about the centre, the ones containing the motivation behind the center of the

circles have their courses out on a comparative side of delta.

Twinned Loop: In this example there are two particular circles, one resting upon or

enclosing the other and the edges, containing the purpose of center have their exit

towards various deltas.

Accidental/Composite/Chance: Accidentals are sure composite examples inside the

whorl amass as they happen once in a while and are framed absolutely by possibility.

These are the mind boggling designs generally made out of various arrangements in a

single example, for example, rose curve and circle, or circle and whorl and so forth or

whatever other such blends which don't fit properly in the essential example writes.

In any case, most scientists like to additionally group these classes into an aggregate of five

classes; Arch, Tented Arch , Whorl, Left Loop and Right Loop.

These groupings have been done in light of the edge stream example of the fingerprints.

Visual investigation uncovers that the above-said classes have following qualities:

Introduction

14

Arch: The stream of edges frames a curve like portrayal at the focal area of the unique

mark.

Tented Arch: In this class, the arch frames a sharp top at the middle.

Whorl: In this class, a whorl like edge development has been made at the middle.

Left Loop: In a few fingerprints, the edge streams like a loop and curves towards left

course. These fingerprints are named Left Loop.

Right loop: Like the past class, when the loop twists towards right, it has been named

Right Loop.

1.2.5 Fingerprint Detection Methods

Fingerprints are the most conclusive kinds of physical proof [24]. Every one of the items at

the criminal scene ought to be considered as conceivable confirmation of fingerprints.

Fingerprints left by the criminal at the criminal scene are known as 'Chance prints'. These

prints are left by the criminal unknowingly, and are appropriately called as 'thieves going

by cards'. There are three primary classes of chance prints as portrayed underneath:

Visible prints: These are the prints shaped when fingers are spread with some hued

material, for example, paint, ink, earth, blood, or other visible material. Prints of this

compose are less regularly found, in light of the fact that the individual who abandons

them can without much of a stretch see and take care to expel them. In the event that by

any shot they are as yet discovered then they could be there because of scramble or

heedlessness. Prints of this nature need not to bother with any improvement. They can be

effortlessly recorded by taking photos with or without utilization of channels.

Plastic prints: These are for the most part found on flexible surface and are called plastic

prints. Plastic prints are for the most part found on items, for example, cleanser, mud,

pitch, candles, thick dried blood, dissolved wax or paraffin, cements et cetera. Prints of this

nature can be captured by precise enlightenment. Fingerprints on flame can likewise be

increased by moving over them a thick layer of printer's ink by methods for elastic roller

and after that taking a photo.

Latent prints: In law enforcement the important and widely used evidences by forensic

agencies worldwide, are latent fingerprints. Latent fingerprints are created after being

unintentionally deposited on the subjects at the crime location, and are of poor quality of

texture quality and large background noise. As a recent advancement in the latent

Introduction

15

fingerprints, Kai Cao and Anil K. Jain [168] have proposed an automated latent fingerprint

recognition algorithm using Convolutional Neural Networks (CNN). The latent is represent

from ridge flow estimation and minutiae descriptor extraction, and extracting

complementary templates.

Different Methods of Fingerprint Development

Laser Method: Laser light has altered the unique mark innovation. Laser technique was

created by Ontario Provincial Police and Xerox Research Center in Canada. Laser takes the

benefit of the way that sweat contains an assortment of segment that fluoresces when lit up

by laser light. It has been discovered that idle unique mark build-up contains an assortment

of natural substances like oils, paint and inks, which have inalienable radiance property.

Utilizing a persistent argon particle laser and seeing through reasonable channels, inert

fingerprints indicate radiance. These prints can be shot utilizing unique channels.

On the off chance that innate radiance neglects to distinguish idle fingerprint through

lasers, at that point the same can be treated with fluorescent material like Coumarin-6 to

give instigated glow to inactive unique finger impression. Exceptionally old prints even as

old as ten years can be produced utilizing laser strategy on surfaces like plastic, elastic,

painted dividers, paper, wood, calfskin and so on. The principle favourable position of

laser bar over the ordinary unique mark advancement strategies is its ability to create

dormant prints that can't be produced by some other strategy. It is exceptionally delicate

technique, and does not experience the ill effects of whenever restriction. The created

prints flaunt especially under laser source. This strategy is exceedingly advanced and can

turn into the technique for decision to be trailed by other customary strategies.

Photographic Method: The prints acquired by this strategy are clear, in any case, in light

of the fact that lone regions in coordinate contact can be shot, the technique has just

restricted an incentive in dermatoglyphic examination and is costly.

New Fingerprint Detection Technology: In a period of cutting edge DNA innovation,

fingerprint confirmation might be viewed as old fashioned criminology; however it's not as

obsolete as a few offenders may think. Propelled fingerprinting innovation now makes

creating, gathering, and distinguishing unique mark confirm simpler and snappier.

Sometimes, notwithstanding attempting to wipe fingerprints clean from a wrongdoing

scene may not work. Not just has the innovation for gathering fingerprint prove enhanced,

Introduction

16

yet the innovation used to coordinate fingerprints to those in the current database has been

fundamentally made strides.

Advance Fingerprint Identification Technology: The FBI propelled its Advance

Fingerprint Identification Technology (AFIT) framework which improved fingerprint

impression and idle print preparing administrations. The framework expanded the

exactness and day by day handling limit of the organization and furthermore enhanced the

framework's accessibility. The AFIT framework executed another fingerprint coordinating

calculation which expanded the precision of fingerprint impression coordinating from 92%

to over 99.6%, as per the FBI. Amid the initial five days of task, AFIT coordinated more

than 900 fingerprints that were not coordinated utilizing the old framework. With AFIT on

board, the organization has possessed the capacity to decrease the quantity of required

manual unique mark audits by 90%.

Prints from Metal Objects: In 2008, researchers at the University of Leicester in Great

Britain built up a method that will upgrade fingerprints on metal articles from little shell

housings to substantial automatic rifles.

They found that substance stores that frame fingerprints have electrical protecting qualities,

which can square electric current regardless of whether the fingerprint material is thin, just

nanometers thick. By utilizing electric streams to store a shaded electro-dynamic film

which appears in the exposed areas between the fingerprints stores, analysts can make a

negative image of the print in what is known as electro-chromic image. As indicated by the

Leicester criminological researchers, this strategy is so delicate it can even recognize

fingerprints from metal questions regardless of whether they have been wiped off or even

washed off with lathery water.

Color-Changing Florescent Film: Professor Robert Hillman and his Leicester partners

have additionally improved their procedure by adding flourophore particles to the film

which is delicate to light and ultra-violet beams. Essentially, the bright film gives

researcher and additional apparatus in creating differentiating shades of inert fingerprints -

electro chromic and brilliance. The bright film gives and third shading that can be

acclimated to build up a high-differentiate unique mark image.

Micro-X-Ray Florescence: The advancement of the Leicester procedure took after a 2005

revelation by University of California researchers working at Los Alamos National

Introduction

17

Laboratory utilizing miniaturized scale X-beam fluorescence, or MXRF, to create unique

finger impression imaging. MXRF recognizes the sodium, potassium and chlorine

components introduce in salts, and also numerous different components, on the off chance

that they are available in the fingerprints. The components are distinguished as an element

of their area on a surface, making it conceivable to "see" a unique mark where the salts

have been kept in the examples of fingerprints, the lines called rubbing edges by legal

researchers. MXRF really identifies the sodium, potassium and chlorine components

display in those salts, and in addition numerous different components, on the off chance

that they are available in the fingerprints. The components are identified as an element of

their area on a surface, making it conceivable to "see" a fingerprint where the salts have

been saved in the examples of fingerprints, the lines called rubbing edges by legal

researchers.

Non-invasive Procedure: The procedure has a few focal points over customary

fingerprint impression location techniques that include treating the speculate zone with

powders, fluids, or vapours keeping in mind the end goal to add shading to the finger

impression so it can be effectively observed and captured. Utilizing customary fingerprint

differentiate improvement, it is infrequently hard to recognize fingerprints introduce on

specific substances, for example, kaleidoscopic foundations, stringy papers and materials,

wood, cowhide, plastic, glues and human skin.

The MXRF system dispenses with that issue and is non-intrusive, which means a

fingerprint dissected by the strategy is left immaculate for examination by different

strategies like DNA extraction. Los Alamos researcher Christopher Worley said MXRF

isn't a panacea for identifying all fingerprints, since a few fingerprints won't contain

enough noticeable components to be "seen". Notwithstanding, it is imagined as a

reasonable buddy to the utilization of conventional differentiation improvement strategies

at wrongdoing scenes, since it doesn't require any concoction treatment steps, which are

tedious, as well as can for all time adjust the proof.

Forensic Science Advances: While numerous advances have been made in the field of

legal DNA prove, science keeps on gaining ground in the field of fingerprinting

improvement and accumulation, making it expanding all the more most likely that if a

criminal deserts any confirmation whatsoever at the wrongdoing scene, he will be

Introduction

18

distinguished. New fingerprint innovation has improved the probability of examiners

creating proof that will withstand challenges in court.

1.2.6 Fingerprint Retrieval

In November 2010, Ministry of Communications and Information Technology,

Government of India announced the fingerprint image and minutiae data standard for e-

governance applications in India [169]. The ISO-19794-4:2005(E) Fingerprint Image Data

Standard is adopted by Indian e-Governance Standard. As per the adopted ISO standard,

the fingerprint image can be stored in either PNG or JPEG2000 with compression ratio up

to 1:15.

The FBI has formulated national standards for digitization and compression of gray-scale

fingerprint images. The compression algorithm for the digitized images is based on

adaptive uniform scalar quantization of a discrete-wavelet transform sub-band

decomposition, a technique referred to as the wavelet/scalar quantization method. The

algorithm produces archival-quality images at compression ratios of around 15 to 1 and

will allow the current database of paper fingerprint cards to be replaced by digital imagery

[182].

The UIDAI (Unique Identification Authority of India) started a project to assign unique

identification number to each resident of India. In Indian context, biometrics were

determined to be the most suitable factors for carrying out de-duplication. Hence it is

necessary to enrol all residents along with their biometrics and build a clean database for

the purposes of a National Identity system. It has adopted ISO/IEC 19794-4 Fingerprint

Image Data Standard as Indian Standard and specified certain implementation values

[183]. Uncompressed images are strongly represented and for legacy reasons, lossless

JPEG 2000 and WSQ compression is accepted. Storage format is permitted as per ISO

Section 8.3. The Enrollment status of the UIDAI project as of Dec 31st 2011 [184] shows

false positive identification rate (FPIR) as 0.057%. In practical terms, it means that at a run

rate of 10 lakh enrolments a day, only about 570 cases need to be manually reviewed daily

to ensure that no resident is erroneously denied an Aadhaar number. The false negative

identification rate (FNIR) is 0.035%. This implies that 99.965% of all duplicates submitted

to the biometric de-duplication system are correctly caught by the system as duplicates.

Introduction

19

Recognizing an obscure test fingerprint impression requires an inquiry against all selected

images in such a vast database and postures testing issues both accordingly time and

precision. To decrease the hunt space required, unique mark retrieval, or called ordering, is

regularly embraced. In such methodologies, each fingerprint is spoken to in a specific

frame so that amid look stage it is conceivable to pre-sift through substantial quantities of

images with low comparability to the test image in view of such a portrayal, and the test

would then be able to be recognized from the rest of the competitors by a balanced

coordinating methodology [25].

Convolutional Neural Networks (CNN) have achieved great success in large-scale image

and video recognition. Karen et al. [15] investigated the effect of CNN depth on its

accuracy in the large-scale image recognition systems. Matthew et al. [186] introduced a

visualisation technique that reveals the input stimuli that excite individual feature maps at

any layer in the model and provided observation on the evaluation of features during

training and diagnosis of potential problems with the model.

FIGURE 1.5: Block Diagram of Fingerprint Retrieval system

Fig. 1.5 shows a typical fingerprint retrieval system. Fingerprint retrieval systems can

essentially separate into three classes: worldwide component, nearby element and

coordinating score based. The retrieval strategies in light of worldwide highlights more

often than not utilize worldwide example of edges with a uniform model, for instance,

Feng and Jain proposed a multistage sifting framework, which uses design compose,

Fingerprint Sensor Feature Extraction

Matching

Database

Fingerprint Sensor Feature Extraction

Enrollent

Authentication

Live

Fingerprint

Live

Fingerprint

Retrieved Images

Introduction

20

solitary focuses and introduction field, and Lee proposed a retrieval system in view of

finger code [26]. The retrieval strategies in the view of neighbourhood include more often

than not use nearby highlights of fingerprint, for example, neighbourhood edge highlights,

particulars highlights and SIFT Features. Coordinating score based retrieval methods use

coordinating score over a little arrangement of reference images as the list. When all is said

in done, nearby highlights are favoured in light of the fact that worldwide highlights may

not be dependably acquired from low quality or fractional images.

Considering that particulars are generally utilized as capacity includes in the greater part of

huge unique fingerprint image databases, and have neighbourhood security which can

guarantee strength when the test image is incomplete, we propose a novel unique mark

retrieval approach in light of details triplet in this paper. In this approach, two procedures

are proposed to repay the moderately frail discriminative energy of details triplet. The first

is a strategy to determine the quantity of coordinated particulars polygons from

coordinating data of details triplets, which offers extra data to assess the similitude of two

particulars sets. The second one is to join a one-piece banner to each of highlight lists to

improve the coordinating accuracy. These two procedures essentially increment the

precision of fingerprint retrieval in vast databases [27].

In unique mark ordering/retrieval, the issue of balanced coordinating is reached out to one-

to-N coordinating without requiring the subject's claim of character. Given a unique mark

occurrence of obscure character, the framework looks through the whole database of

enlisted formats and returns a rundown of likely fingers that the unique finger impression

may have a place with. The unique finger impression highlights prepared the framework

ought to be sufficiently solid to recognize one finger from all the others in the database

[28]. To this point, some revealed works influenced utilization of the introduction to

handle highlights while some investigated the highlights on the complex-sifted images.

Details highlights and other changed highlights were likewise embraced.

Fingerprint ordering calculations, profoundly identified with persistent arrangement,

perform superior to elite grouping by choosing most plausible competitors and arranging

them by the comparability to the info. Nonstop characterization is proposed to manage the

issue of select order by speaking to fingerprint impression with include vectors. The

inquiry is performed by registering the separations between the question and every format

to yield the nearest. Those calculations depend on two sorts of highlights: worldwide

Introduction

21

highlights and nearby highlights. Worldwide highlights speak to the worldwide example of

edges and valleys and the introduction field [29]. They for the most part distinguish a

center point and afterward gauge its neighborhood include.

However such methodologies are bad at taking care of partialness and non-direct twisting,

which are two noteworthy issues in the portrayal of fingerprint highlights, because of their

absence of separation of neighbourhood surfaces. Conversely, neighbourhood highlights

are accepted to be vigorous to halfway and misshaped prints since they can speak to stable

nearby structures. Triplets of details and accessorial data are utilized as a part of the

ordering technique [30].

Without a doubt, nearby highlights are normally spoken to by neighbourhood surface

portrayal around intrigue focuses. In this manner, two conditions are to be met. To start

with, intrigue focuses ought to be steadily identified under various survey circumstances,

particularly scaling and revolution. Second, a particular neighbourhood descriptor ought to

be computed free of the intrigue indicates and be fitting portray the locales. In any case, the

retrieval execution may experience the ill effects of the little amount of particulars focuses

in fractional fingerprints [31].

Fingerprint matching: Fingerprint image matching methods can be comprehensively

delegated being details based, relationship based or edge include based [32]. The execution

of particulars construct methods depend in light of the exact recognition of details focuses

and the utilization of complex coordinating systems based ones. In view of the dampness

substance of the skin, the procured images may have either thin or thick edges. Further, the

nature of the images obtained suing the sensor may fluctuate with time, in this manner

confusing the relationship plans. Particulars are an arrangement of focuses in a plane and

these focuses are coordinated in a layout. In connection based, in two fingerprint images

relative pixels are thought about. Edge include based is a propelled innovation that catch

and looks at the edges.

1.3 Motivation

The motivation of this research work is for developing a need to differentiate a man for the

security purpose. The Fingerprint impression is one of the prevalent biometric strategies

which are utilized to confirm person. A precision of the arrangement demonstrate assumes

a key part in Fingerprint impression retrieval. Retrieval speed is bargained because of the

Introduction

22

substantial scale databases of Fingerprint images. Retrieval precision is diminished

because of clamour and twisting in the Fingerprint images. The proposed strategy ought to

enhance the retrieval exactness with the new grouping model.

1.4 Objective of Research

A fingerprint image classification system should be able to analyse and recognize the

universal classes; Arch, Left Loop, Right Loop and Whorl. The recognition of the

fingerprint image class should be done accurately and without any human intervention. A

fingerprint image retrieval system should be able to retrieve efficiently the similar images

from the image dataset. The main objective of this research work is to develop a system

which first, recognize the class of the query fingerprint image and then retrieve similar

images from the dataset of recognized class.

There are two major objectives of this research work as follows:

• To develop an image classification model that extracts hybrid and robust features for the

fingerprint images. The extracted features are to be used for the classification of

fingerprint image.

• To develop an image retrieval model that uses that extracts the scale and rotation

invariant features to represent the fingerprint images uniquely. The extracted features

are to be used for the retrieval of similar images from the similar fingerprint image

class.

1.5 Research Contribution

In this thesis, method of fingerprint image classification and retrieval system is suggested

with the combinations of feature extraction, classification and retrieval techniques.

Fingerprint images are taken from NIST special database 4 (NIST-4), Fingerprint

Verification Competition databases (FVC2000, FVC2002 and FVC2004) and the

fingerprint images captured using optical sensor with the help of volunteers. The main

contributions of this thesis are summarized as follows:

The feature extraction methods using LBP and GLCM are used collectively to

extract features from fingerprint images. The concatenated features are given to the

Support Vector Machine classifier for classification. K-nearest neighbor (k-NN)

classifier is also used for the comparison of the classification results.

Introduction

23

The Speed-Up Robust Feature (SURF) algorithm is used to extract the features to

test BoF classifier along with the hybrid feature extraction method. Both the

classification methods are evaluated on NIST-4 and captured fingerprint images

databases.

In retrieval phase, Scale Invariant Feature Transform (SIFT) features and SURF are

used collectively to extract features from the fingerprint images of the same class.

The concatenated features are extracted from these methods are given to BoF and

other distance matching algorithms for indexing. The retrieval methods are

evaluated on NIST-4, FVC and captured fingerprint image databases.

1.6 Organization of the Thesis

Chapter 1 deals with the general introduction of the research work. It elaborates about

basics of biometrics, fingerprints and its applications, fingerprint classification and

fingerprint retrieval. Chapter 2 presents the details about the research work done in the

same area and their conclusions.

Three types of methods are proposed in the thesis and each one is explained briefly in

chapter 3, 4 and 5. Chapter 3 discusses the two feature extraction methods LBP and GLCM

in detail with application of it on developing hybrid fingerprint classification system. The

effectiveness of the method is discussed in form of results at the end of the chapter.

Chapter 4 presents the detail discussion of SURF algorithm to extract fingerprint image

features. Chapter 5 proposes hybrid approach towards developing fingerprint retrieval

system. Hybrid system is discussed using SURF and SIFT algorithms, to extract scale and

rotation invariant features. The similarity ranking is performed by using BoF classifier.

Chapter 6 contains the main conclusions of all the work proposed in this thesis. It also

explains the possible work to be carried out in the future.

Theoretical Background and Literature Survey

24

CHAPTER – 2


2.1 Introduction

Research on biometric techniques has increased restored consideration as of late expedited

by an expansion in security concerns. The current world state of mind towards terrorism

has affected individuals and their administrations to make a move and be more proactive in

security issues. This requirement for security likewise reaches out to the requirement for

people to ensure, in addition to other things, their workplaces, homes, individual belonging

and resources. Numerous biometric procedures have been produced and are being

enhanced with the best being connected in regular law authorization and security

applications. Biometric strategies incorporate a few best in class systems. Among them,

unique finger impression acknowledgment is thought to be the most capable strategy for

most extreme security validation [33].

Advances in sensor innovation and an expanding interest for biometrics are driving a

blossoming biometric industry to grow new advances. As business motivating forces

increment, numerous new advances for person identification are being created, each with

its own qualities and shortcomings and a potential specialty showcase. This section audits

some notable biometrics with uncommon accentuation to unique finger impression.

2.2 Biometric Security:

The expression "Biometrics" is gotten from the Greek words "bio" (life) and

"measurements". Computerized biometric frameworks have just turned out to be accessible

in the course of the most recent couple of decades, due to the significant progresses in the

field of PC and image handling. In spite of the fact that biometric innovation appears to

have a place in the twenty first century, the historical backdrop of biometrics goes back a

great many years [34]. The old Egyptians and the Chinese assumed an expansive part in


25

biometrics history. Today, the emphasis is on utilizing biometric face acknowledgment, iris

acknowledgment, retina acknowledgment and recognizing qualities to stop terrorism and

enhance safety efforts. This segment gives a short history on biometric security and

fingerprint recognition.

The main systematic approach for capturing hand and finger images for ID reasons for

existing was utilized by Sir William Herschel, Civil Service of India, who recorded an

impression on the back of an agreement for every laborer to recognize representatives.

S. Falohun et al. [35] developed a system which estimates the human age-range and gender

using fingerprint analysis trained with Back Propagation Neural Network and DWT+PCA.

A total of 280 fingerprint samples of people with various age and gender were gathered.

140 of these samples were used for training the system‘s Database; 70 males and 70

females respectively. This was done for age groups 1-10, 11-20, 21-30, 31-40, 41-50, 51-

60 and 61-70 accordingly. In order to determine the gender of an individual, the Ridge

Thickness Valley Thickness Ratio (RTVTR) of the person was put into consideration.

M. ManiRoja [36] proposes two different approaches based on RSA and Elliptic curve

cryptography for database protection of biometric authentication systems. Use of K-means

algorithm is also proposed for the generation of encryption and decryption keys from the

biometric templates. In this paper, implementation of public key algorithms has been

realized for experimental purposes and the results thus obtained have been critically

verified.

Francisco Fons et al. [37] As proof-of-concept, a run-time partially reconfigurable field-

programmable gate array (FPGA) is addressed to carry out a specific application of high-

demanding computational power such as an automatic fingerprint authentication system

(AFAS). Biometric personal recognition is a good example of compute-intensive algorithm

composed of a series of image processing tasks executed in a sequential order.

Chinta Someswara Rao et al. [38] discovered that no two irises are similar and their

discoveries were granted a patent amid 1986. The following stage in unique finger

impression mechanization happened toward the finish of 1994 with the Integrated

Automated Fingerprint Identification System (IAFIS) competition. The opposition

distinguished and researched three major challenges; Digital unique finger impression

procurement, Local edge trademark extraction and Ridge trademark design coordinating.


26

The principal Automated Fingerprint Identification System (AFIS) was produced by Palm

System.

2.3 Fingerprint Recognition System

FIGURE 2.1: Typical Fingerprint-based Biometric System

Fingerprint imaging innovation has been in presence for a considerable length of time.

Archeologists have revealed prove recommending that enthusiasm for fingerprints dates to

ancient times. In Nova Scotia petroglyphs demonstrating a hand with overstated edge

designs has been found [39]. In old Babylon and China, fingerprints were impressed

claytablets and seals. The utilization of fingerprints as an extraordinary human identifier

goes back to second century B.C. China, where the character of the sender of an imperative

archive could be confirmed by his fingerprint impression in the wax seal.

In fourteenth-century Persia fingerprints were impressed different authority papers. Around

then, a legislative authority watched that no two fingerprints were precisely

indistinguishable. Sachin Dwivedi et al. [40] described the edges of the fingerprints as

circles and spirals yet did not take note of their incentive as a method for individual ID.

This was the initial move towards the cutting edge investigation of fingerprints. The

principal present day utilization of fingerprints happened in 1856 when Sir William

Herschel, the Chief Magistrate of the Hooghly region in Jungipoor, India, had a nearby

agent, Rajyadhar Konai, inspire his imprint on the back of an agreement. Afterward, the

correct list and center fingers were printed alongside the mark on all agreements made with

local people.

The object was to panic the underwriter of renouncing the agreement on the grounds that

Fingerprint

Image

Acquisition

Feature

Extraction

Pattern

Matching

Accept

or

Reject?

Database


27

local people trusted that individual contact with the report made it all the more

authoritative. As his fingerprint accumulation developed, Waits et al. [41] started to

understand that fingerprints could demonstrate or invalidate personality. In spite of his

absence of logical information in fingerprinting he was persuaded that fingerprints are

remarkable and perpetual all through life.

Further branch of this new field concerns approaches was developed by Mark S. Nixon et

al. [42] to estimate soft biometrics, either using conventional biometrics approaches or just

from images alone. These three strands combine to form what is now known as soft

biometrics. We survey the achievements that have been made in recognition by and in

estimation of these parameters, describing how these approaches can be used and where

they might lead to. The approaches lead to a new type of recognition, and one similar to

Bertillonage which is one of the earliest approaches to human identification.

For the following thirty years, Bertillon age was the primary strategy for biometric

identification. Dr. Henry Faulds, British Surgeon-Superintendent of the Tsukiji Hospital in

Tokyo, took up the investigation of fingerprints in the 1870's after noticing finger engraves

on ancient ceramics. In 1880, in the October 28 issue of the British logical periodical

Nature, Dr. Faulds was the first to distribute ascientific record of the utilization of

fingerprint impression as a method for ID. Notwithstanding perceive the significance of

fingerprints, for ID he devised a strategy for grouping too.

Dr. Faulds is credited for the first fingerprint distinguishing proof in light of a fingerprint

left on a liquor bottle. The method of characterization proposed by Dr. Faulds is called

Henry Classification system and depends on examples, for example, circles and whorls,

which is still used to day to sort out fingerprint card records.

Proceeding with crafted by Dr. Faulds et al. [43] set up the singularity and lastingness of

fingerprints. This book, "Fingerprints" from 1892, contains the principal unique finger

impression characterization system containing three essential example composes: circle,

curve, and whorl. The framework was based on the appropriation of the example composes

on the ten fingers, e.g. LLAWLLWWLL. The framework worked, yet was yet to be

enhanced with a classification that was simpler to control.

Sir Galton recognized the attributes utilized for personal ID, the one of a kind edge quality

known as minutiae, which are regularly alluded to as "Galton's details". In 1892, Juan


28

Vecetich, an Argentine Police official, made the first criminal unique finger impression

distinguishing proof. He could distinguish a lady, who had murdered her two children and

cut her own throat trying to maintain a strategic distance from blame. Her bleeding print

was left on a doorpost, demonstrating her way of life as the killer.

2.4 Fingerprint Acquisition Methods

This area shows the different procurement techniques used to acquire fingerprints of an

individual [44].

Optical: Optical fingerprint imaging includes catching a computerized image of the

print utilizing unmistakable light. This kind of sensor is, fundamentally, a particular

advanced camera. The best layer of the sensor, where the finger is set, is known as the

touch surface. Underneath this layer is a light-emitting phosphor layer, which

illuminates the surface of the finger. The light reflected from the finger goes through the

phosphor layer to a variety of strong state pixels (a charge-coupled gadget), which

catches a visual image of the unique finger impression. A scratched or filthy touch

surface can cause an awful image of the fingerprint.

A disservice of this sort of sensor is the way that the imaging abilities are influenced by

the nature of skin on the finger. For example, a filthy or stamped finger is hard to image

appropriately. Additionally, it is feasible for a person to disintegrate the external layer

of skin on the fingertips to the point where the unique mark is never again noticeable. It

can likewise be effortlessly tricked by a image of a fingerprint if not combined with a

"live finger" finder. Be that as it may, dissimilar to capacitive sensors, this sensor

innovation isn't helpless to electrostatic release harm.

Ultrasonic: Ultrasonic sensors influence utilization of the standards of restorative

ultrasonography with a specific end goal to make visual images of the fingerprint.

Dissimilar to optical imaging, ultrasonic sensors utilize high recurrence sound waves to

enter the epidermal layer of skin. The sound waves are produced utilizing piezoelectric

transducers and reflected vitality is likewise estimated utilizing piezoelectric materials.

Since the dermal skin layer shows a similar trademark example of the fingerprint, the

reflected wave estimations can be utilized to frame an image of the unique finger

impression. This dispenses with the requirement for perfect, undamaged epidermal skin

and a spotless detecting surface.


29

Capacitance: Capacitance sensors use the standards related with capacitance to shape

fingerprint images. In this technique for imaging, the sensor cluster pixels each go

about as one plate of a parallel-plate capacitor, the dermal layer which is electrically

conductive goes about as the other plate, and the non-conductive epidermal layer goes

about as a dielectric.

Passive capacitance: A passive capacitance sensor utilizes the guideline illustrated

above to shape an image of the fingerprint designs on the dermal layer of skin. Every

sensor pixel is utilized to quantify the capacitance by then of the cluster. The

capacitance differs between the edges and valleys of the fingerprint because of the way

that the volume between the dermal layer and detecting component in valleys contains

an air hole. The dielectric consistent of the epidermis and the territory of the detecting

component are known esteems. The deliberate capacitance esteems are then used to

recognize unique finger impression edges and valleys.

Active capacitance: Dynamic capacitance sensors utilize a charging cycle to apply a

voltage to the skin before estimation happens. The utilization of voltage charges the

compelling capacitor. The electric field between the finger and sensor takes after the

example of the edges in the dermal skin layer. On the release cycle, the voltage over

the dermal layer and detecting component is contrasted against a reference voltage all

together with figure the capacitance. The separation esteems are then computed

numerically, and used to frame a image of the fingerprint. Dynamic capacitance

sensors measure the edge examples of the dermal layer like the ultrasonic technique.

Once more, this disposes of the requirement for spotless, undamaged epidermal skin

and a perfect detecting surface.

The Automatic Fingerprint Recognition Systems require a reasonable clamor free unique

finger impression keeping in mind the end goal to process it for particulars discovery or

relationship. This is finished by pre-processing the unique fingerprint.

2.5 Fingerprint Pre-processing

The fingerprint must be pre-processed to remove the impact of commotion, impact of

dryness, wetness of the finger and distinction in the applied weight while checking the

fingerprint. The pre-processing is a multi-step process [45]. The distinctive methods used

in pre-processing are; Smoothening Filter, Intensity Normalization, Orientation Field


30

Estimation, Fingerprint Segmentation, Ridge Extraction and Thinning. Fig. 2.2 shows the

fingerprint pre-processing flow [188].

FIGURE 2.2: Typical flow of fingerprint image pre-processing

Depending on the application and highlight extraction strategy these means may change.

Galbally et al. [46] have proposed an adaptive image sifting strategy for peculiarity

(particulars) conservation. Initially image quality is evaluated by Fourier range of the

image, fingerprint image pre-processing is performed in light of the discriminant

recurrence and measurable surface highlights. Later Gaussian separating is utilized to

upgrade the edge structure and angle field lucidness quality is utilized for division of

region of interest (ROI).

A Short Term Fourier Transform (STFT) based image separating is talked about by

Chikkerur et al. [47] to upgrade fingerprint images. Another approach broadly took after

depends on Gabor Filter, Gabor channels equipped for directional sifting of edges. The

directional band pass Gabor channel bank approach is a standout amongst the best and

numerically exquisite strategies to date for fingerprint image upgrade, this reality is

utilized by different scientists for fingerprint image improvement and division. Like

directional Gabor channels Sahasrabudhe et al. [48] have proposed directional Fourier

channels for sifting fingerprint edges.

The execution of Gabor channel or some other directional sifting relies on the course of

channel, which ought to be appropriately tuned to edge bearing, for this the introduction

estimation is vital. Most generally utilized approach is to experience the slopes of dim

power. There are some different strategies accessible in writing like channel bank based

approach, ghastly estimation, waveform projection, however the inclination based

Fingerprint Image

Enhancement

Input Fingerprint Image

Fingerprint Image

Segmentation

Enhanced foreground fingerprint image


31

technique give better outcomes. Angle based system likewise have varieties, scientists

have proposed distinctive approaches to assess introduction shape inclinations.

Püspöki et al. [49] has proposed a straightforward procedure in view of direct count of

introduction in light of slope. In creators have examined introduction estimation in view of

Eigen estimations of nearby structure tensor. Bazen has examined PCA and Structure

tensor construct introduction estimation calculation situated in light of inclinations.

Galar et al. [50] have proposed a changed strategy in light of angle estimation which

misuses the way that the introduction field have a tendency to be persistent in the

neighboring areas, he has proposed a calculation which appoints the introduction of main

issue in view of the introduction of neighboring squares at four corners and their field

quality additionally called as coherence. Hong et al. talked about an instrument to

accomplish a smoother introduction field by a constant vector field approach. They utilize

an averaging channel to the consistent vector recorded ascertained from the neighborhood

inclination point. Both the methodologies give sensibly great guess of the introduction

field. They have proposed a calculation for introduction estimation in light of improved

neighborhood averaging of slope fields. This calculation accomplished smoother

introduction field by social occasion data from neighborhood.

Fingerprint division comprises in the partition of the finger impression territory (frontal

area) from the foundation. Division systems abuse the presence of a situated periodical

example in the forefront, and a non-arranged isotropic example out of sight. The technique

portrayed by Jain et al. depends on the neighborhood sureness level of the introduction

field, which is processed utilizing the force inclination of the image. Those 16×16 pixel

obstructs in which the sureness level is higher than a given edge are considered as frontal

area pieces.

Patel et al. [51] suggested that the normal angle on each square is figured, which is relied

upon to be high in the forefront (edge valley varieties) and low out of sight.

Bian et al. [52] have proposed a strategy where different parameters, for example,

inclination rationality, dim force mean and change are additionally utilized as a part of the

division choice. A morphological post-handling is likewise performed with a specific end

goal to fill the rest of the gaps in the frontal area and additionally out of sight. This strategy

is exceptionally exact however includes high computational weight. The strategy


32

introduced by Mehtre relies on the angle and results in bring down computational weight.

It registers the dark level difference over the normal direction of the introduction field,

which is relied upon to be high in nearness of edge valley variety. This technique is

actualized in other unique finger impression confirmation frameworks too.

The division method introduced by Thai et al. [53] depends on Gabor channels. It registers

the reaction of eight oriented Gabor channels to decide if a square has a place with the

closer view or to the foundation. It is demonstrated that when good quality images are

viewed as, both angle and Gabor-based strategies create comparable outcomes, however

Gabor channel based methodsare speedier than inclination based methodologies. In this

work, an improved Gabor channel based approach is exhibited.

Rajasekaran et al. [54] have proposed an upgraded approach for fingerprint division in

view of the reaction of eight arranged Gabor filters. This technique gets higher closer view

measure and impressively bring down size of the foundation area, hence recovering blocks

with particulars and legitimate yet not very much characterized zones. A deficiency of this

strategy is that the thresholding isn't programmed and a manual limit should be chosen

exactly. We have proposed programmed thresholding in view of Gabor channels; the

procedure is mechanized by producing an edge by Otsu's strategy connected on Gabor

greatness histogram.

In Correlation based fingerprint acknowledgment framework we have to decide an

enlistment point as a kind of perspective; this is called as centre point. Centre point

location is a non-trifling assignment. In this examination they talked about connection

based unique finger impression acknowledgment; now we talk about a few strategies for

centre point location.

Cao and Anil Jain [55] have performed Core Point Detection utilizing Integration of Sine

Component of the Fingerprint Orientation. In this technique the sine part of the

introduction recorded is incorporated in a semi-roundabout district, with three fragments

and the segments are directly summed up in a particular way as talked about in, this

strategy give a decent estimate of fingerprint yet precision is still low, and for better guess

more number of emphases are required.

They have talked about another approach in view of count of Poincare list of the

considerable number of focuses in introduction outline, actually determine the Poincare list


33

by figuring the back to back focuses field edge distinction and summing it, the point

encased by a computerized bend (Core Point) will have most noteworthy Poincare file. The

Poincare Index outline then edge and the point with most noteworthy esteem are taken as

centre point. This technique is additionally utilized by Iwasokun et al. [56]. This strategy is

likewise recursive, if no centre point is discovered then the introduction field is

smoothened and again a similar methodology is taken after. In the event that still center

point isn't evaluated then the creators have recommended a covariance based strategy, yet

this is computationally costly.

A Core Point Estimation strategy utilizing Direction Codes and Curve Classification is

created by Khodadoust et al. [57]. In this strategy first the introduction field is figured,

from this field the directional codes are produced. The course codes are utilized for rough

estimate and an inspected grid is produced. This framework and bend characterization

strategy like chain codes is utilized for precise centre point recognition. This technique

requires more advances and the system given in this paper isn't appropriate for curve

compose prints, since it isn't conceivable to characterize the centre point for this situation.

Cao et al. [58] have utilized Complex convolution delineate centre and delta focuses over

the squared introduction field to get the centre and delta point in the unique finger

impression. This technique covers recognition of centre and delta focuses, and it is a

multistep procedure.

The precision got is great around 95-98%. In the meantime the scientific multifaceted

nature is high and the technique needs post preparing steps too. They have built up a centre

point identification calculation in light of different highlights got from the fingerprint

which are by and large utilized for steady center point recognition. Here Orientation field

is utilized, rationality, Poincare record for center point location. In spite of the fact that all

fingerprints don't have center point still this calculation is helpful to recognize high shape

districts and gives high precision as it joins focal points frame singular highlights. The

following stage in the improvement of fingerprint acknowledgment frameworks is

including extraction and coordinating. They pass the pre-prepared image as a contribution

to this.

2.6 Fingerprint Retrieval

Recently, new optical encryption schemes were proposed, which greatly enrich the


34

research of optical image encryption. Ran et al. [59] proposed an optical image encryption

system based on cascaded FRFT, which expand the dimensions of the secret key space and

was easier for optical implementation. In recent years, image encryption methods based on

DRPE were testified unsafe due to their vulnerability to attacks. An encryption method

using fingerprint as a secrete key based on DRPE was proposed in, which could effectively

resist known plaintext attack (KPA). However, encryption systems based on DRPE are still

deficient. In 2010, Qin and Peng proposed the asymmetric cryptosystem (ACS) based on

phase-truncated Fourier transforms (PTFT), which reserved the two phases as private keys

using PTFT. Owing to the nonlinear operation of phase truncation, the robustness against

existing attack schemes was improved.

Later on analysts proposed different encryption plans in view of PTFT. However exhibited

from the point of view of cryptanalysis that PTFT was defenseless against particular

assault and KPA. As of late, Liu et al. proposed ACS in view of twofold irregular stage

encoding utilizing secluded arithmetic operation and Yang-Gu calculation individually and

Rajput et al. [60] proposed Fresnel space nonlinear optical image encryption plot in view

of Gerchberg– Saxton calculation. They gave new plans to optical asymmetric

cryptosystem (OACS).

In any case, late investigations demonstrated that current OACS is untenable due to the

misconception of essential guideline of ACS. Furthermore, Sui et al. [61] proposed an

optical various levelled authentication scheme in view of obstruction and hash work which

understood the mix of optics and cryptography. In the plan two-shaft interferometer

achieved the confirmation procedure and the hash work significantly enhanced the security

of the framework.

The previous uses data identified with the example of edges and valleys found in

fingerprints to parcel the unique finger impression database into shared elite containers. In

this sense, once the fingerprint query image is grouped, the image hopefuls are looked in

the comparing container. Further, this sort of approach can be subdivided into four

subcategories relying upon the kind of data utilized for elite characterization, to be

specific, edge, introduction field, singularity, and basic based data.

In ceaseless characterization approaches, fingerprint images are spoken by include vectors.

Similitudes among unique fingerprints are built up by the separation in the component

space of their relating highlight vectors. This approach is firmly identified with a


35

fingerprint database ordering issue. Edge based methodologies customarily utilize the data

of the structure recurrence of the unique finger impression edges for grouping purposes.

The work proposed by Bhattacharya et al. [62] considers the recurrence range of

fingerprints, acquired by applying a hexagonal Fourier Transform, to group fingerprints

into three classes: whorls, circles, and curves. A wedge-ring locator is utilized to parcel the

recurrence space images into non-covering zones in which the pixel esteems are summed

up to shape an element vector. Once the component vector is discovered, it is contrasted

with the reference include vectors of every one of the classes and a further grouping is

performed by utilizing a closest neighbor characterization technique. To catch the structure

of unique finger impression edges, a few works create numerical models to describe the

comparing images.

Janan et al. [63] use, for instance, B-splines bends to approximate the state of the

fingerprint edges. At that point, comparative introduction edges are assembled together to

acquire a worldwide shape portrayal of the fingerprints which is utilized for grouping.

Methodologies in light of introduction fields utilize the neighborhood normal introductions

of fingerprint edges to group fingerprints.

Jayaraman et al. [64]] utilize the piece introduction fields of fingerprints and conviction

measures to produce the fingerprint highlight vectors. For highlight dimensionality

decrease, they consider a SOM neuronal system to enhance the general order precision.

fingerprint singularities have been generally utilized for characterization. They can be

characterized as the nearby districts where the fingerprint edges introduce some physical

properties.

Ala et al. [65] separate the singularities that can be found in the fingerprints to order them

by thinking about the area and the quantity of the identified singularities. Auxiliary

methodologies utilize the topology data of fingerprints for arrangement purposes

Peralta et al. [66] section the orientational field of fingerprint images to speak to the

fingerprints as social diagrams. For each class of fingerprints, social diagrams demonstrate

is made. An inexact chart coordinating calculation is utilized to arrange fingerprint images.

In spite of the fact that the inquiry spaces can be diminished in restrictive arrangement

approaches, there are a few inadequacies that ought to be considered:


36

A few fingerprints display properties of more than one class and in this way they can't

be appointed to only one canister.

Regular dispersion of fingerprints isn't uniform and hence, even by performing binning

in the first database, the quantity of one-to-numerous examinations can in any case be

high.

A portion of the qualities utilized for binning are difficult to identify because of the

nearness of commotion, encompassing conditions, and so forth.

Thanh-Nghi Do et al. [67] demonstrated that the circulation of fingerprint classes isn't

uniform (93.4% of fingerprints are among an arrangement of three classes). Then again,

Murillo-Escobar et al. [68] proposed a ceaseless framework to record fingerprint databases

utilizing streak hashing. For that reason, the area and introduction of the particulars, and

additionally the quantity of edges among them, are utilized to produce highlight vectors.

Some data identified with the component vectors are acquired and used to make the image

records that are added to a multi outline structure and thought about later amid the unique

finger impression recovery.

Khan et al. [69] proposed fingerprint impression ID impression. Robert Hastings built up a

technique for improving the edge design by utilizing a procedure of situated dissemination

by adjustment of anisotropic dispersion to smooth the image toward the path parallel to the

edge stream. The image force shifts easily as one navigate along the edges or valleys by

expelling the vast majority of the little irregularities and breaks yet with the personality of

the individual edges and valleys saved.

Paulino et al. [70] proposed a strategy for fingerprint impression check which incorporates

both particulars and model based introduction field is utilized. It gives vigorous prejudicial

data other than details focuses. fingerprint coordinating is finished by joining the choices

of the matchers in light of the introduction field and details.

Lomte et al. [71] proposed a strategy for execution measure of nearby administrators in

fingerprint by recognizing the edges of fingerprints utilizing five neighborhood

administrators specifically Sobel, Roberts, Prewitt, Canny and LoG. The edge

distinguished image is additionally divided to extricate singular fragments from the image.


37

Sutthiwichaiporn et al. [72] exhibited a strategy by presenting a unique space finger

impression improvement technique which breaks down the fingerprint image into an

arrangement of separated images then introduction field is evaluated. A quality veil

recognizes the recoverable and unrecoverable tainted locales in the info image are

produced. Utilizing the assessed introduction field, the information fingerprint image is

adaptively improved in the recoverable locales.

Marasco et al. [73] purposed a strategy to explore the impact of five diverse power levels

on fingerprint coordinating execution, image quality scores, and details check amongst

optical and capacitance finger impression sensors. Three images were gathered from the

correct forefingers of 75 members for each detecting innovation. Expressive insights,

examination of change, and Kruskal-Wallis nonparametric tests were directed to evaluate

noteworthy contrasts in particulars tallies and image quality scores in view of the power

level.

The outcomes uncover a critical contrast in image quality score in view of the power level

and every sensor innovation, yet there is no huge distinction in particulars tally in light of

the power levels of the capacitance sensor. The image quality score, appeared to be

affected by power and sensor compose, is one of numerous components that impact the

framework coordinating execution, yet the evacuation of low quality images does not

enhance the framework execution at each power level.

Jung et al. [74] proposed a technique to describe a fingerprint coordinating in view of lines

extraction and chart coordinating standards by receiving a mixture plot which comprises of

a hereditary calculation stage and a neighborhood look stage. Test comes about exhibited

the power of calculation.

Venkatesh et al. [75] proposed a technique for evaluating four course introduction field by

considering four stages, I) pre-processing fingerprint image, ii) deciding the essential edge

of finger impression square utilizing neuron beat coupled neural system, iii) assessing

piece heading by projective separation fluctuation of an edge, rather than a full square, iv)

remedying the assessed introduction field.

Zhang et al. [76] represented an auto ID framework for fingerprints. Vital bends are

generated by utilizing key diagram calculation by kegl. Feature extraction calculation,

from key bends, is utilized to extract uniqueness of fingerprint images. The experimental


38

results indicates that bends acquired from diagram calculation are smoother than the

diminishing calculation.

Galbally et al. [77] developed a design acknowledgement with two classes. A strategy for

feature extraction based on fingerprint and its way to deal with the classification is

discussed. The SVM is applied on the feature vector and used to categorize the fingerprints

in to real or non-real groups.

Wong et al. [78] presented a strategy to overcome non straight mutilation utilizing Local

Relative Error Descriptor (LRLED). The calculation comprises of three stages I) a couple

savvy arrangement technique to accomplish fingerprint arrangement ii) a coordinated

details combine set is acquired with a limit to decrease non-coordinates at long last iii) the

LRLED – based likeness measure. LRLED is great at recognizing comparing and non-

relating details combines and functions admirably for fingerprint particulars coordinating.

Chatbri et al. [79] displayed a strategy, diminishing is the way toward lessening thickness

of each line of examples to only a solitary pixel width. The necessities of a decent

calculation regarding a unique finger impression are I) the diminished unique fingerprint

acquired ought to be of single pixel width without any discontinuities ii) Each edge ought

to be diminished to its focal pixel iii) Noise and particular pixels ought to be disposed of

iv) no further evacuation of pixels ought to be conceivable after consummation of

diminishing procedure.

Yang et al. [80] exhibited fingerprint order framework utilizing Fuzzy Neural Network.

The fingerprint highlights, for example, particular focuses, positions and bearing of center

and delta acquired from a binarised fingerprint. The strategy is delivering great

arrangement comes about.

Chhillar et al. [81] has created anoid technique for Fingerprint acknowledgment. Edge

bifurcations are utilized as particulars and edge bifurcation calculation with barring the

noise– like focuses are proposed. Exploratory outcomes demonstrated the humanoid

unique finger impression acknowledgment was hearty, dependable and quick.

Wang [82] proposed a strategy for quick singularities seeking calculation which utilizes

delta field Poincare list and a fast arrangement calculation to characterize the unique finger

impression in to 5 classes. The location calculation looks through the heading field which

has the bigger course changes to get the singularities. Singularities discovery is utilized to


39

expand the exactness.

Ahmed et al. [83] Proposed fingerprint upgrade to enhance the coordinating execution and

computational proficiency by utilizing a image scale pyramid and directional sifting in the

spatial area.

Guo et al. [84] introduced auxiliary approach with fingerprint characterizations by utilizing

the directional image of fingerprint rather than procedure utilizing a dim level watershed

strategy to discover the edges introduce on a unique fingerprint by specifically checked

fingerprints or inked singularities. Directional image incorporates overwhelming heading

of edge lines.

Gaikwad et al. [85] have built up a strategy for extraction of particulars from fingerprint

images utilizing midpoint edge form portrayal. The initial step is division to isolate frontal

area from foundation of unique fingerprint. A 64 x 64 area is removed from fingerprint

image. The grayscale powers in 64 x 64 locales are standardized to a consistent mean and

fluctuation to evacuate the impacts of sensor clamor and grayscale varieties because of

finger weight contrasts.

After the standardization the differentiation of the edges are upgraded by sifting 64 x 64

standardized windows by fittingly tuned Gabor channel. Handled unique fingerprint is then

checked start to finish and left to right and advances from white (foundation) to dark

(closer view) are recognized. The length vector is computed in all the eight headings of

shape. Each shape component speaks to a pixel on the form, contains fields for the x, y

directions of the pixel. The proposed technique takes less and don't recognize any false

details.

Zhou et al. [86] proposed Scale Invariant Feature Transformation (SIFT) to speak to and

coordinate the unique finger impression. By removing trademark SIFT highlight focuses in

scale space and perform coordinating in view of the surface data around the element

focuses. The blend of SIFT and traditional particulars based framework accomplishes

essentially preferred execution over both of the individual plans.

Chaudhari et al. [87] have acquainted consolidated strategies with fabricate a minutia

extractor and a minutia matcher. Division with Morphological activities used to enhance

diminishing, false particulars evacuation, minutia stamping.

Peralta et al. [88] proposed a compelling and effective calculation for details extraction to


40

enhance the general execution of a programmed unique finger impression distinguishing

proof framework since it is vital to save genuine particulars while expelling deceptive

particulars in post-preparing. The proposed novel fingerprint image post-preparing

calculation attempts an endeavors to dependably separate deceptive details from genuine

ones by making utilization of edge number data, alluding to unique dim level image,

planning and orchestrating different handling systems appropriately, and furthermore

choosing different handling parameters precisely. The proposed post-handling calculation

is successful and proficient.

Marasco et al., [89] has developed a portrayal method for fingerprint recognizable proof. It

effectively represents the qualities of fingerprint to generate unique distinguishing roof.

The fingerprint images are shifted in various ways and a 640-dimensinal feature vector is

generated in the focal area of each fingerprint image. Hence, the feature vector is shortened

and requires merely 640 bytes. Euclidean distance is used for registering between the

format finger code and the information finger code. The technique gives great coordinating

with high exactness.

Arjona M [90] presented Directional Fingerprint Processing utilizing unique finger

impression smoothing, order and ID in view of the solitary focuses (delta and center

focuses) got from the directional histograms of a finger impression. Fingerprints are

ordered into two primary classes that are called Lasso and Wirbel. The procedure

incorporates directional image development, directional image square portrayal, solitary

point recognition and choice. The technique gives coordinating choice vectors with least

mistakes, and strategy is straightforward and quick.

Tiwari et al. [34] proposed an ordering calculation like Jayaraman et al. [64] calculation.

They utilized a coaxial Gaussian track code (CGTC) rather than the MBP. The calculations

depend vigorously on the centre point. These calculations have not considered the absence

of centre point, in curve compose fingerprints. These calculations additionally utilized 72

segments that every minutia is lying in a part. At the point when a details was put in

covering zones, limit between two segments, their calculations have not considered this

unique case.

As of late, Khodadoust & Khodadoust [92] proposed a unique finger impression ordering

calculation in view of particulars sets and uncommon sort of centre point, arched or upper

centre point. This calculation abuses level-1 and level-2 includes and uses circle properties.


41

Since the quantity of details sets are (n(n − 1)/2), consequently behind calculations think

about O(n2) particulars sets amid coordinating.

It is observed from the literature that the automated systems based on biometrics, specially

human fingerprints, have been capitalized since long. The various approaches have been

used for the classification and retrieval of images, targeting specific applications, but still

there is a scope in improvement.

Classification using LBP and GLCM Features with SVM / kNN Classifiers

42

CHAPTER – 3

Classification using LBP and GLCM Features

3.1 Introduction

3.1.1 Fingerprint Classification

Fingerprint classification provides an important indexing mechanism in a fingerprint

database. An accurate and consistent classification can greatly reduce fingerprint matching

time for a large database. Hybrid approaches combine two or more approaches for

classification. These approaches show some promise but have not been tested on a large

database.

For example, Fitz and Green [190] used frequency-based approach on 40 fingerprints.

They used the frequency spectrum of the fingerprints for classification, whereas Kawagoe

and Tojo [191] used another structure-based approach on 94 fingerprints. Chong et al.

[192] reported results on 89 fingerprints in a structure-based approach that used B-spline

curves to represent and classify fingerprints.

Birdi et al. [100] proposed a fingerprint classification algorithm based on the multispace

Karhunen-Loève (KL) transform applied to the orientation field. The classification

accuracy reported by the various authors are different in databases with different numbers

of fingerprints, and therefore, they cannot be directly compared. Most of the work in

fingerprint classification is based on supervised learning and discrete class assignment

using knowledge-based features.

Bernard et al. [189] proposed the utilization of Kohonen topologic guide for unique finger

impression design arrangement. The learning procedure considers the expansive intra-class

assorted variety and the continuum of fingerprint design composes. Contingent upon the

unique finger impression area particular learning and the master approach, they displayed a

unique and natural portrayal of the calculation. They reasoned that the self-sorting out


43

maps are proficient in fingerprint grouping and gave a decent nonlinear bi-dimensional

guess of a heading map dissemination of highlight space.

Mikel et al. [172] reviewed various classification approaches used by various researchers.

Classifiers like SVM, Neural Networks, Graph matching, fixed classification and Nearest

Neighbour has been widely used. In fixed classification approaches, training is not required

as the classification is done along with the feature extraction process. The classification

experiments are performed on NIST-4 in most of the cases, while in few cases FVC dataset

is also used.

There are few challenges to the classification of the fingerprints. Fingerprints from the

same finger will be slightly different whenever they are captured. Every time when a finger

is pressed on a surface of an acquisition tool, it is applied with a certain amount of pressure

and angle. These parameters vary from time to time. Hence, fingerprint classifiers are

expected not to be sensitive to translations and rotations. Another challenge is related to

fingerprint variation. There are cases in which large intra class variation and small inter-

class variation is observed. In some cases, fingerprint contains the properties of more than

one class. These challenges makes fingerprint classification problem difficult and also

provides motivation to develop continuous classification schemes.

3.1.2 Feature Extraction

In pattern recognition and image processing, feature selection and extraction is a special

technique used for selecting a subset of most relevant image characteristics that can best

represent the whole image. Transforming the input digital image data into a set of features

is called feature extraction. If the method of extraction is designed efficiently, then the

subsequent recognition task can be performed with this reduced representation instead of

the full sized input image. The minutiae and non-minutiae features are the two frequently

used features extracted from a fingerprint image for various fingerprint applications.

There are two non-minutiae features such as Local Binary Pattern (LBP) and Gray Level

Co-occurrence Matrix (GLCM) offer multiple advantages like being rotation invariant, free

from gray scale invariance, robust to noise and small skin distortions. The advantages of

using the local features in fingerprint representation are that they are computed at multiple

points in the image, and hence, they are invariant to image scale and rotation. In addition,

they do not need further image pre-processing or segmentation operations.


44

Bazen and Otterlo [98] show that reinforcement learning can be used for minutiae

detection fingerprint matching. Minutiae are characteristic features of fingerprints which

determine their uniqueness. Classical approaches use a series of image processing steps for

this task, but lack robustness because they are highly sensitive to noise and image quality.

They propose a more robust approach in which an autonomous agent walks around in the

fingerprint and learn how to follow ridges in the fingerprint and how to recognize

minutiae. The agent is situated in the environment and uses reinforcement learning to

obtain an optimal policy.

Rahimi et al. [99] presented a novel algorithm for the detection of singular points in

fingerprint images. The number and location of singular points are used to classify

fingerprint images into five general groups and therefore to narrow down the search space

in a large fingerprint database. Using the proposed directional masks in the first step, they

detect the neighborhood of the singular points. In the second stage, using the proposed

algorithm, an adaptive singular point detection method is implemented to extract the exact

location of core and delta points. In their findings, they stated that a good minutiae

extrication algorithm should efficiently incorporate local ride orientation into ridge

extraction operation. However, it should also be kept in mind that directional smoothing is

usually a computationally expensive operation. Post-processing is a very important step in

minutiae extraction, which can eliminate a significant number of errors.

3.1.3 Methods of Feature Extraction

FIGURE 3.1: Taxonomy of features used for fingerprint classification [172]

Mikel et al. [172] has presented a review on the various feature extraction approaches used

for fingerprint classification in the literature. As shown in Fig. 3.1, the feature extraction

methods, based on orientations include syntactic coding, direct / unaltered coding,

registration and segmentation. The methods based on number / positions and relative

measures are categorized under the singular point approach. The ridge structure approach

Features for Classification

Singular

Points

Orientations Filter

Responses

Ridge

Structure


45

includes ridge tracing, skeleton, geometric / global representation model and minutiae map.

The filter response approaches are the use of Gabor and other filters like FFT, DCT, GHM

discrete multi-wavelet transform etc.

In the area of feature extraction various methods are discussed and compared with their

strength and weaknesses. The weakness becomes the motivation for further invention.

According to the survey on fingerprint classification by Mikel et al. [194] PCASYS

(Pattern-level Classification Automation SYStem) is well-known since it is the only

method whose source code was made publicly available by the authors. PCASYS uses the

features which are the orientations which are obtained from the registered Orientation

Maps (OM). Primarily, two components from the squared orientation map is used to

generate future vector. By using Karhunen-Loève (KL) transform (also known as Principal

Component Analysis (PCA)), the reduced feature vector is generated from the feature

vector, obtained primarily.

The most relevant contribution in the field of fingerprint image classification is done by

Anil Jain and Karu [193]. Despite of its simplicity, the proposed work described a

fingerprint classification model based on fixed rules and obtained good results though.

After detecting the singular points (SP), the rules like whether there are SPs present or not.

If no SPs are present, Arch class is declared.

Based on the detection of two pairs of cores and deltas, whorl or no whorl is declared. If

only one pair of core and delta is found, from core to the delta, a straight line is traced and

the difference with the orientations crossing this line is taken in to account to decide loops

and tented arch. Based on the positions of SPs in the image, the decision of left loop or

right loop is made. As the SPs are required to obtained to decide the fingerprint image

class, the OM is obtained by slit sum method and then processed. By giving high weights

to the orientations near to the centre of the image, OM is processed so that OM is

smoothed and hence, the noise in the outer locations are also removed.

Again the use of OM is proposed by Cappelli et al. [195]. The segmentation of OM,

consists of grouping areas with similar orientation is used. A cost function is defined as the

orientation variance in the different areas. The OM is matched with the templates,

predefined per fingerprint class. The lowest segmentation cost, obtained for each class,

generates feature vector. The fingerprint is enhanced and improved using PCASYS. As

explained earlier, OM extraction is performed using slit sum method. Regularization,


46

attenuation and strengthening steps are further used to enhance OM. After obtaining OM, it

is fitted to masks, of each class.

To define the masks, vertices, regions defined by the vertices and the relationships between

pairs of regions and functions which define mobility of the vertices are used. To carry out

the matching in a sequential manner, first, by using greedy search, the best global

positioning is obtained and then, for each mobile vertice, the optimal position is obtained.

Finally, the minimum cost obtained for each mask, normalized by the sum of costs is

codified in the feature vector.

Nyongesa et al. [196] proposed a method which uses the features like; angle between

cores, deltas and angles between each one of the cores and the deltas. The angle

differences between the SPs detected are used to compose feature vector. The method of

identifying angles between cores and deltas describe six features. Along with this features

another feature is also used which is computed as a mean orientation in a window of 16x16

blocks. This block is centred in the centre of the image.

Before extracting the features, the image is pre-processed in which, fingerprint image is

binarized using the mean intensity in each block of 8x8 pixels. By using gray level

variance method, the OM is extracted and smoothed using the window of 15x15. To

obtain SPs, Poincarè method is used. Then feature vector is computed based on these

extracted points.

As proposed by Shah [104] the features of the feature vector are based on orientations in

two different ways: directed/unaltered and registered. The method of obtaining orientations

and registration highly differ from the works represented. A line detection mechanism is

used to extract the orientations. The final feature vector consists of two parts, the first part

consists of the features of direct orientations and the second part consists of the features

which are registered ones. As a part of pre-processing, the fingerprint is binarized in local

windows of 16x16.

An interactive line detection method is carried out to compute the OM. To extract the

orientations, eight oriented masks are considered. Skeletonization process is used as a line

detection mechanism. Once the lines are detected, the two parts of the feature can be

obtained. For the first part of the feature vector, the lines are converted into an OM. As the


47

other part of the feature vector, the most predominant orientations and their percentage of

occurrence in which OM is divided.

A comparative summary of features and classification techniques used by authors with

fingerprint images, is shown in Table 3.1 [172] [194].

TABLE 3.1: Comparative Summary of feature extraction and classification methods

Author Name Year Features Classification

Technique Test Dataset

R. Cappelli, D. Maio

and D. Maltoni 2002 Orientation Map

Nearest

Neighbour NIST-4

J.-H. Chang and K.-

C. Fan 2002 Ridge Structure Graph Matching NIST-4

R. Cappelli, D.

Maio, D. Maltoni

and L. Nanni,

2004 Orientation Map Nearest

Neighbour NIST-4/14

X. Wang and M.

Xie, 2004

Singular Points

Ridge Structure

Fixed

Classification NIST-4

J.-K. Min, J.-H.

Hong and S.-B. Cho 2006 Filter Responses SVM NIST-4

T. Kristensen, J.

Borthen and K.

Fyllingsnes

2007 Filter Responses Neural Networks Other

J. Li, W.-Y. Yau and

H. Wang 2008

Singular Points

Ridge Structure SVM NIST-4

H.-W. Jung and J.-H.

Lee, 2009 Ridge Structure Structural Models NIST-4

U. Rajanna, A. Erol

and G. Bebis 2010

Orientation Map

Ridge Structure

Nearest

Neighbour NIST-4

K. Cao, L. Pang, J.

Liang and J. Tian 2013 Ridge Structure

SVM and Nearest

Neighbour NIST-4

J. Luo, D. Song, C.

Xiu, S. Gen and T.

Dong

2014 Filter Responses Nearest

Neighbour NIST-4

H.-W. Jung and J.-H.

Lee, 2015 Orientation Map

Nearest

Neighbour

FVC2000, FVC

2002 &

FVC2004

As it can be seen from Table 3.1 that Support Vector Machines (SVM) and Nearest

Neighbour classifiers are frequently used for the fingerprint classification purpose by

researchers, concatenated features of GLCM and LBP are classified using SVM and kNN

classifiers and results are compared. SVM is inherently used for binary classification. This

technique is needed to extend the method to handle the real world classification problems

involving more than two classes. There are two main approaches for class classification.

First is One Vs One Classifier, also known as pairwise classifier. It constructs k(k−1)/2

pairwise binary classifiers. The classifying decision is made by aggregating the outputs of


48

the pairwise classifiers. Another one is One Vs All Classifier, known as ―One against

Rest‖. This classifier trains binary classifiers, each of which separates one class from the

other k (k −1) classes. Classification of a test image, is done by computing the function

value for each binary SVM model [179].

3.2 Proposed Methodology

Most of the datasets of fingerprint images contains gray scale images. Co-occurrence

matrix provides a statistical approach that can well describe texture image [177]. Since

1973, introduced by Haralick [176], Gray level co-occurrence matrix (GLCM) is popular

feature extraction technique for image classification and retrieval. The limitation of GLCM

features of describing information regarding the relative spatial position of pixels with one

another. Local Binary Pattern (LBP) makes it possible to describe the texture and shape of

a digital image. This is done by dividing an image into several small regions from which

the features are extracted. These features consist of binary patterns that describe the

surroundings of pixels in the regions. The obtained features from the regions are

concatenated into a single feature histogram, which forms a representation of the image.

Images can then be compared by measuring the similarity (distance) between their

histograms [178]. To combine the strong characteristics of GLCM and LBP, concatenation

of these two features is used as input to the classifiers for improved classification.

3.2.1 Gray level co-occurrence matrix (GLCM)

A statistical approach that can well describe second-order statistics of a texture image is a

co-occurrence matrix [109]. GLCM is essentially a two-dimensional histogram in which

the (i, j) th element is the frequency of event i co-occurs with event j. A co-occurrence

matrix is specified by the relative frequencies P(i, j , d, θ) in which two pixels, separated

by distance d, occur in a direction specified by the angle θ, one with gray level i and the

other with gray level j. A co-occurrence matrix is therefore a function of distance r, angle θ

and gray-scales i and j.


49

FIGURE 3.2: Co-occurrence matrix directions for extracting texture features

Our observation is that a fingerprint image can be decomposed into regions with regular

textures. So we should be able to represent these regular texture regions by using co-

occurrence matrices. To do so, we utilize the co-occurrence matrices in angles of 0°, 45°,

90°, and 135° as follows:

jnmIilkIdnImkLLLLnmlkdjiP crcr ),(,),(,||,0,,,#)0,,,(

(3.7)

}),(,),()||,(

)||,()()()),(),,{((#)45,,,(

jnmIilkIdnIdmk

ordnIdmkLLLLnmlkdjiP crcr

(3.8)

}),(,),(,0||,)()()),(),,{((#)90,,,( jnmIilkInIdmkLLLLnmlkdjiP crcr

(3.9)

}),(,),()||,(

)||,()()()),(),,{((#)135,,,(

jnmIilkIdnIdmk

ordnIdmkLLLLnmlkdjiP crcr

(3.10)

Where, # denotes the number of elements in the set.

Feature Extraction Using GLCM

To understand the formation of co-occurrence vectors, take an example of an image vector

of size 4x4 as shown in the Fig. 3.2 (a). Co-occurrence vector is computed for the fixed d,

which is 1 in our case for θ=0˚, 45˚, 90˚, and 135˚ as described by Haralick et al. [176]. So

we have 4x4 co-occurrence matrices. Based on each computed co-occurrence matrices, 4

features that can successfully characterize the statistical behaviour of a co-occurrence

matrix are extracted. They are energy, mean, homogeneity and standard deviation.

135 [-1,-1]

90 [-1,0]

45 [-1,1]

0 [0,1]

Reference Pixel


50

FIGURE 3.3:GLCM construction based on a (a) test image along four possible directions (b) 0º (c) 45º (d)

90º and (e) 135º with a distance d = 1.

Energy (E) can be defined as the measure of the extent of pixel pair repetitions. It measures

the uniformity of an image. When pixels are very similar, the energy value is large.

ji

jipEnergy,

2),(

(3.11)

Mean gives the estimation of the intensity in the relationship that contributes.

nm

jip

Meanji

,

),(

(3.12)

where, m and n are number of rows and columns respectively.

Homogeneity refers to the closeness of distribution of elements to the GLCM diagonal.

ji ji

jipyHomogeneit

, ||1

),(

(3.13)

Measure of heterogeneity is strongly correlated to Standard Deviation. It increases when

the gray level values differ from their mean.

m

i

n

j

meanjipmn 1 1

2)),((1

Deviation Standard

(3.14)

0 3 3

3 0 0

3

0 0

1

2

3

1 2 3 0

0

1

1

2

2

3

3

0

3

0

2

2

1

3

0

#

0 1 0 1

0 0 4

1

4 0

2

0

0

2 0 0 1

0

1

1

2

2

3

3

0

3

0

2

2

1

3

0

#

0 0 4 2

4 0 0

2

0 0

1

1

4

1 1 4 0

0

1

1

2

2

3

3

0

3

0

2

2

1

3

0

#

0 2 0 0

0 2 0

0

0 2

1

0

3 0

1 0 0 2

0

1

1

2

2

3

3

0

3

0

2

2

1

3

0

#

0

0 2 3

1 0 2

0

1 3

0

3

2

2 0 1 3

(a)

(d) (c) (b) (e)


51

3.2.2 Local Binary Patterns (LBP)

The first LBP operator was introduced by Ojala et al [175]. The operator works with the

3x3 neighbourhood of every pixel. It is a gray-scale invariant texture measure computed

from the analysis of a 3x3 local neighborhood over a central pixel. It may not catch the key

surface attributes. Therefore, the first LBP is reached out to utilize the area of various sizes

[111]. They utilized round neighbourhoods and directly introduced the pixel esteems in the

image. Along these lines, LBP could be utilized as a part of any range and any quantities of

pixels in the area. They utilized the documentation (P, R), where P is the quantity of

examining focuses on the hover of span R. The LBP estimation of pixel (gc) from the dark

estimations of the circularly symmetrical neighbourhood gp (p = 0,…, P-1) is figured by

1

0

, 2*)(P

p

p

cpRP ggsLBP

(3.15)

00

01)(

xif

xifxs

(3.16)

Where gc is the centre pixel value, gp is one of the eight surrounding centre pixel values, R

is the radius, P is the whole neighborhood number and s(x) is the sign function.

1 2 3

4 5 6

7 8 9

Threshold

0 0 0

1 1

1 1 1

X

1 2 4

8 16

32 64 128

0 0 0

8 16

32 64 128

LBP = 16+32+64+128 = 240

FIGURE 3.4: Example of LBP operator

Texture Description using LBP

The histogram of the LBP labels can be used as a texture descriptor. The original LBP with

P neighborhoods has 2p different binary patterns. However, Ojala et al. [175] observed that

the natural images generally contain a small number of LBP codes, which are called the

uniform LBP. When the bit pattern is considered circular, the uniform LBP includes the

maximum of two bitwise transitions which ranges from 0 to 1 and vice versa. These

uniform patterns represent the majority of texture microstructures [112].


52

FIGURE 3.5: Process of generating LBP features

Feature Extraction using LBP

LBP are a local texture descriptor that have performed well in different computer vision

applications, including texture classification and segmentation, image recovery, surface

assessment, and face detection. The best current strategy for fingerprint liveness

recognition utilizes this technique. Another augmentation to the original operator is the

meaning of alleged uniform examples, which can be utilized to lessen the length of the

feature vector and execute a simple rotation invariant descriptor.

The number of various marks of LBP is lessened from 256 to only 10 in the uniform

pattern. The standardized histogram of the LBPs with 256 and 10 containers for non-

uniform and uniform administrators individually is utilized as a feature vector. The

presumption underlying the calculation of a histogram is that the dispersion of patterns

matters, however the correct spatial area does not. In this way, the upside of removing the

histogram is spatial invariance property. The LBP separated images are similarly

partitioned in rectangles and their histograms are linked to frame the final feature vector

[113].

3.2.3 Fingerprint Classification using Support Vector Machine (SVM)

SVM is a supervised learning model to recognize spatial relationships between groups of

data. It seeks to find the optimal separating hyper-plane between classes by focusing on the

training cases that lie at the edge of the class distributions. It maps the original input space

into a higher-dimensional feature space using a certain kernel function, making possible

the avoidance of the computation of the inner product of two vectors. Once the instances

are mapped to the feature space, the optimal separating hyper plane with the maximal

margin is computed. This way, an upper bound of the expected risk is minimized instead of


53

the empirical risk. In order to carry out the training of the SVMs, Platt [197] suggested the

Sequential Minimal Optimization (SMO) procedure.

FIGURE 3.6: Hyper-plane for classifying samples in SVM

SVM is utilized to train the classifier with feature vector. Treating the data as two sets of

points in an n-dimensional space, SVM builds a separating hyper plane by Lagrangian

multipliers to differentiate the negative data points from the positive ones. Intuitively, a

good separation is achieved when a separating hyper plane has the largest distance to the

boundary points of both classes. Chih-Wei et al. [200] presented a practical guide for

support vector classification using LIBSVM software [199]. The radial basis function

(RBF) kernel is widely suggested as the best choice by the users. It also provides a tool

named ―Cross-validation and Grid-search‖ to search the appropriate parameters for RBF

kernel. LIBSVM with RBF kernel is used to train classifiers in the experiments, and the

tool ―Cross validation and Grid-search‖ is utilized to search the penalty parameter and the

kernel parameter.

3.2.4 Fingerprint Classification using kNN

kNN classifier is best suited for classifying biometric features based on their images due to

its lesser execution time and better accuracy than other commonly used methods which

include Hidden Markov Model and Kernel method [173]. Although methods like SVM and

Adaboost algorithms are proved to be more accurate than KNN classifier, KNN classifier

has a faster execution time and is dominant than SVM. [174].


54

The training data is mapped into multi-dimensional element space. The feature space is

divided into locales in light of the nearest highlight space in the training set. The training

data is mapped into multidimensional feature space. The element space is apportioned into

locales in view of the classification of the preparation set. A point in the element space is

alloted to a specific class in the event that it is the most continuous classification among

the K closest preparing information.

In the classification stage, this classifiation method finds the K nearest named training tests

for an unlabeled info test and allocates the information test to the classification that seems

most frequently inside the K subset. This classification approach is exceptional with its

straightforwardness. It performs well even in dealing with the arrangement tasks with

multi-ordered document. It finds the gathering of the k nearest instances in the training set

to the test examples. From these k neighbor occurrences a choice is made in light of the

power of a specific class. As a result, both the distance metric used to process the closeness

of the cases and the number of neighbors considered are the key components in this

strategy. With a specific end goal to locate the best value for these parameters a cross-

approval strategy can be taken after utilizing the accessible training data [115].

FIGURE 3.7: Example for kNN

Under this scheme an image in the test set is recognized by assigning to it the label of the

closest point in the learning set, where distance are measured in image space. The

Euclidean distance metric is often chosen to determine the closeness between the data

points in KNN. A distance is assigned between all pixels in a dataset. Distance is defined

as the Euclidean distance between two pixels. The distance between the neighbours can be

calculated using the following formula.


55

2

),( ii qpqpd (3.17)

The Hamming distance between the neighbours can be calculated by the following

equation.

||),( ii qpqpd (3.18)

3.2.5 Combining GLCM and LBP

FIGURE 3.8: Classification flow using Fusion features with SVM / KNN

The features of the input image are calculated using both the GLCM and LBP. Then both

the extracted features are concatenated to generate final feature vector. The model library

is populated with the features of training images of fingerprint. Classification is carried out

with SVM and kNN classifiers.

3.3 Experimental Results

The proposed methods are simulated on Intel Core i5, 2.60 GHz processor, 64-bit

Operating System in MATLAB 2016a environment. The effectiveness of the method is

Input Image

Calculate GLCM

Feature Extraction

using concatenation Model Data Library

Data Model Population

Calculate LBP

Classification

Feature Matching to

Determine Class

Display Result

Input Image

Calculate GLCM

Feature Extraction

using concatenation

Calculate LBP


56

measured by applying it on standard dataset, i.e. NIST – 4 which contains 800 images of

fingerprints (200 images of each class).

(a) (b)

FIGURE 3.9: Sample images from the dataset used; a) NIST – 4, and b) Captured

NIST - 4 dataset is a valuable tool for evaluating fingerprint systems on a statistical sample

of fingerprints which is evenly distributed over the five major classifications. A

comparative summary shown in Table 3.1, this database is widely used for verifying

fingerprint classification techniques proposed by the researchers. This dataset contains 8-

bit gray scale fingerprint image pairs including classifications in Arch, Left and Right

Loops, Tented Arch, Whirl classes. Suitable for automated fingerprint classification

research, the database is widely used for algorithm development: system training and

testing.

3.3.1 Classification using SVM

A confusion matrix is a table that is often used to describe the performance of a

classification model or a classifier on a set of test data for which the true values are known.

True positives and true negatives can be computed from the confusion matrix as explained

in chapter 1, section 1.1.1. Overall accuracy of the classifier can be computed from true

positives and true negatives as:

Classification Accuracy = (True Positives + True Negatives) / Total Size (3.19)

Cross Validation is used to assess the predictive performance of the models and to judge

how they perform outside the sample to a new data set also known as test data. K-fold

cross validation (k=10) is used to analysed the classification accuracy. The experiments

have been carried out using various images for testing purpose arbitrarily. The testing

images are selected by the system arbitrarily and five different testcases are considered. In

all testcases, testing images are excluded from training phase. The results of various test

cases are shown further.


57

Fig.3.9 to Fig. 3.13 demonstrates the confusion matrices of the fingerprints with fusion

features and SVM classifier. Fig. 3.15 shows the classification rate of various testcases and

Fig. 3.16 shows the classification rate of various classes.

Arch 18 2 0 0 90.00

Left

Loop 2 14 0 4 70.00

Right

Loop 4 2 13 1 65.00

Whorl 0 5 1 14 70.00

Arch Left

Loop

Right

Loop Whorl

73.75%

(Over all

Accuracy)

FIGURE 3.10: Confusion matrix of Testcase1 using SVM Classifier

Arch 13 5 2 0 65.00

Left

Loop 4 12 1 3 60.00

Right

Loop 2 1 15 2 75.00

Whorl 1 0 2 17 85.00

Arch Left

Loop

Right

Loop Whorl

71.25%

(Over all

Accuracy)



58

Arch 18 1 1 0 90.00

Left

Loop 3 17 0 0 85.00

Right

Loop 0 2 16 2 80.00

Whorl 2 1 2 15 75.00

Arch Left

Loop

Right

Loop Whorl

82.50%

(Over all

Accuracy)


Arch 16 2 2 0 80.00

Left

Loop 5 10 1 4 50.00

Right

Loop 2 2 14 2 70.00

Whorl 2 3 0 15 75.00

Arch Left

Loop

Right

Loop Whorl

68.75%

(Over all

Accuracy)



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Arch 15 2 2 1 75.00

Left

Loop 2 18 0 0 90.00

Right

Loop 2 0 13 5 65.00

Whorl 1 2 2 15 75.00

Arch Left

Loop

Right

Loop Whorl

76.25%

(Over all

Accuracy)


FIGURE 3.15: Analysis of Results of various Testcases (with SVM Classifier)

73.75 71.25

82.5

68.75 76.25

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Testcase1 Testcase2 Testcase3 Testcase4 Testcase5

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FIGURE 3.16: Analysis of Results of various classes (with SVM Classifier)

3.3.2 Classification with kNN Classifier

The efficiency of proposed method has been investigated using kNN classifier also. The

experiments have been carried out on various test cases consisting of varied input classes

of fingerprints. The input fingerprint images are chosen randomly and have been excluded

from training phase. The results of such five cases are shown further.

Fig.3.16 to Fig. 3.20demonstrates the confusion matrices of the fingerprints with fusion

features and kNN classifier. Fig. 3.21 shows the classification rate of various testcases and

Fig. 22 shows the classification rate of various classes.

85

60 67.5

72.5

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Arch Left Loop Right Loop Whorl

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Arch 16 0 2 2 80.00

Left

Loop 3 10 3 4 50.00

Right

Loop 4 3 8 5 40.00

Whorl 2 3 5 10 50.00

Arch Left

Loop

Right

Loop Whorl

55.00%

(Over all

Accuracy)

FIGURE 3.17: Confusion matrix of Testcase1 using kNN Classifier

Arch 13 1 4 2 65.00

Left

Loop 5 8 0 7 40.00

Right

Loop 6 3 10 1 50.00

Whorl 3 1 4 12 60.00

Arch Left

Loop

Right

Loop Whorl

53.75%

(Over all

Accuracy)



62

Arch 13 5 1 1 65.00

Left

Loop 8 11 0 1 55.00

Right

Loop 5 2 10 3 50.00

Whorl 5 2 2 11 55.00

Arch Left

Loop

Right

Loop Whorl

56.25%

(Over all

Accuracy)


Arch 13 2 4 1 65.00

Left

Loop 4 14 0 2 70.00

Right

Loop 2 1 11 6 55.00

Whorl 5 3 2 10 50.00

Arch Left

Loop

Right

Loop Whorl

60.00%

(Over all

Accuracy)



63

Arch 11 2 4 3 55.00

Left

Loop 3 13 1 3 65.00

Right

Loop 9 3 7 1 35.00

Whorl 2 4 6 8 40.00

Arch Left

Loop

Right

Loop Whorl

48.75%

(Over all

Accuracy)


FIGURE 3.22: Analysis of Results of various Testcases (with kNN Classifier)

Overall classification accuracy using SVM and kNN classifiers for different testcases are

shown in Fig. 3.15 and Fig. 3.22 respectively. The overall classification accuracy is found

better with SVM classifier. In both classifications, using SVM and kNN, Arch class shows

better classification accuracy. This can be observed from Fig. 3.16 and Fig. 3.23.

55 53.75 56.25 60

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FIGURE 3.23: Analysis of Results of various classes (with kNN Classifier)

3.4 Concluding Remarks

The classification system proposed in this chapter demonstrates the use of fusion of two

texture features of fingerprint images. As discussed earlier, the efficiency of each

fingerprint classification system highly depend on feature extraction method. And to make

the part of feature extraction robust, the feature vector is built using LBP and GLCM

features. This feature vector is given as input the classifier.

Various experiments have been performed to analyse the effectiveness of the proposed

method. The experiments have been conducted for various test cases including different

fingerprint images of various classes using SVM as well as kNN and results are analysed.

It has been observed that compared to kNN, method works better with SVM classifier and

achieves classification rate ranges from 68.75 % to 82.50 % for various test cases. The

experiments have also been conducted on various fingerprint classes individually. The

classes of different fingerprints have been chosen arbitrarily and results are examined.

Looking at the classification rate, it has been concluded that arch are most prominent class

that have been classified most correctly with 85% classification rate using SVM classifier.

The remaining fingerprints are less correctly recognized due to the limitation of method for

proper differentiation of feature points between the classes.

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Classification using Robust Features with Bag of Features Classifier

65

CHAPTER – 4

Classification using Bag of Features (B0F)

4.1 Introduction

Classification of fingerprint images is being adapted as a very important step in image

searching applications like fingerprint recognition and retrieval due to its ability to

minimize the similarity matching count. Fingerprint classification, which classifies

fingerprints into a number of pre-specified categories, provides an indexing scheme to

facilitate efficient matching for large fingerprint databases; if two fingerprint images are

the impressions of the same finger, then they must belong to the same category. Therefore,

a query fingerprint needs to be compared only with the database fingerprints of the same

category in the fingerprint matching process. The central problem in designing a

fingerprint classification algorithm is to determine what features should be used and how

categories are defined based on these features [119].

One of the vital issue in computing the image similarity is feature extraction. The feature

extraction methods discussed in chapter 3 using the concatenation of GLCM and LBP is

time consuming. This chapter discusses a speedy feature extraction method using speeded

up robust features (SURF). SURF helps in detecting the key points which describe the

texture uniquely. It generates unique invariant description of each point detected which

uniquely identifies the point and can be used to match the points even if target image

suffers from distortion due to image noise, illumination, scale change, rotation change and

change of the viewpoint from which the image was taken [202].


66

4.2 Methodology

4.2.1 Feature Extraction using Speeded Up Robust Features (SURF)

For the classification of fingerprint images, SURF are used to extract the local robust

features. As per an article by Bay et al. [201] SURF uses scale and rotation invariant

interest point detector and descriptor which outperforms with respect to repeatability,

distinctiveness, and robustness, yet can be computed and compared much faster.

Following steps are used for extracting features using SURF:

Interest point detection: An interest point detector based on Hassian Matrix is used. The

descriptor vector is generated on the basis of points identified in this step [201][203]. The

detector relies on integral images to reduce the computation time and therefore it is also

called as ‘Fast-Hessian‘ detector [125].

First of all, an integral image is computed from the input image as it helps speed up the

calculation of any rectangular area, especially the Haar wavelets. For a given point (x, y) in

an image, an integral image I can be described as shown in Eq. follows,

yj

j

xi

i

jiIyxI00

),(),(

(4.1)

For a given a point X = (x, y) in an image I, the Hessian matrix H(x, σ) in X at scale σ is

defined as follows [204],

),(),(

),(),(),(

xLxL

xLxLxH

yyxy

xyxx (4.2)

(x, σ) is the convolution of the Gaussian second order derivative with the image I at point

X. Lxx(x, σ), Lxy(x, σ) and Lyy(x, σ) are second order Gaussian derivatives. Gaussians are

optimal for scale-space analysis. In practice, however, the Gaussian needs to be discretised

and cropped, and even with Gaussian filters aliasing still occurs as soon as the resulting

images are sub-sampled [128].

Then, the second-order Gaussian derivates are approximated based on box filters and are

denoted as Dxx, Dxy and Dyy instead. The Hessian determinant is computed from these

terms as follows [201],


67

2)(*)det( wDxyDDH yyxxapprox (4.3)

The Hessian matrix is computed for different filter sizes, where the filter size represents

the region around which the matrix determinant is computed, with various scale factors.

The process is repeated for various octaves. After calculating the Hessian matrix at

different scale factors (different octaves and filter sizes), the interest points are chosen by

computing the local maxima (in a 3x3x3 neighborhood) in scale and image space [16].

This step is O (mn log2 (max (m, n))) for a m × n size image (m is the height of the image

and n is the width in pixels).

Descriptor Generation: For feature description, SURF uses Wavelet responses in

horizontal and vertical direction, which is the coarse grain pixel contrast of a rectangular

region, around a given interest point. A neighbourhood of size 20s x 20s is taken around

the keypoint where s is the size. It describes how pixel intensities are distributed within a

scale dependent neighborhood of each interest point [204]. Typically descriptor vectors of

length 64 or 128 are used.

FIGURE 4.1: Haar wavelet for gradient calculation in (a) x-direction and (b) y-direction

The computing of descriptor vector determines a reproducible orientation that is identified

based on a circular region surrounding the interest point [203]. To assign the orientation,

Haar wavelet responses (in x and y direction) are computed in a circular neighborhood of

6s (where s is the scale factor at which the interest point was identified).

The dominant orientation is estimated by calculating the sum of all responses within a

sliding orientation window (covering an angle of л/3). The longest vector (a sum of

horizontal and vertical responses) is chosen as the dominant orientation. After computing

the dominant orientation, the descriptor vector is computed by choosing a square region of

size 20s (where s is the scale factor at which the interest point was identified). The region

chosen around the interest point and oriented along the dominant orientation is split up into

(a) (b)


68

smaller 4x4 square sub-regions and Haar wavelet responses (dx in the x and dy in the y

direction) are calculated within each of the sub-regions at 5x5 regularly spaced sample

points.

The wavelet responses are summed up in each region and the following four dimensional

vectors per 4x4 region forms the descriptor vector [201]:

|||,|,, yxyx ddddv (4.4)

This vector gives SURF feature descriptor with total 64 dimensions. Lower the dimension,

higher the speed of computation and matching, but provide better distinctiveness of

features [123].

4.2.2 Fingerprint Classification using Bag of Features (BoF)

The basic idea of using Bag-of-Feature (BoF) approach is that a set of local image patches

is sampled using a keypoint detector and a vector of visual descriptors is evaluated on each

patch independently (e.g. SURF descriptor, normalized pixel values). The resulting

distribution of descriptors in descriptor space is then quantified by using vector

quantization against a pre-specified codebook to convert it to a histogram of votes for (i.e.

patches assigned to) codebook centres and the resulting global feature descriptor vector is

set as a characterization of the image. This feature vector is used for classification [205].

BoF methods have been applied to image classification, object detection, image retrieval,

and even visual localization for robots. BoF approaches are characterized by the use of an

order-less collection of image features [139]. Lacking any structure or spatial information,

it is perhaps surprising that this choice of image representation would be powerful enough

to match or exceed state-of-the-art performance in many of the applications to which it has

been applied. Due to its simplicity and performance, the Bag of Features approach has

become well-established in the field..


69

FIGURE 4.2: Example for Bag of Feature Classification

Liu [206] has discussed image feature representation based on BoF. As a first step, key

point detection is performed. Popular key point detectors introduced are Laplacian of

Gaussian (LoG), Difference of Gaussian (DoG), Harris Laplace, Hessian Laplace, Harris

Affine, Hessian Affine, Maximally Stable Extremal Regions (MSER) [207][208]. In next

step, local descriptors describe key point (identified by detectors) neighbourhood. Popular

descriptors are SIFT, LBP and SURF [206].

For the quantization of the descriptors into visual words it is required to generate

vocabulary performed in the next step. By clustering, each cluster is treated as a unique

visual word of the vocabulary. K-means clustering technique is proposed as effective

clustering technique [209]. In local descriptor quantization, one local descriptor is assigned

to one or multiple visual words. To search for the nearest neighbour anomg the vocabulary

obtained in the vocabulary generation step is the common way to assign a descriptor to

visual word(s).


70

As BoG is a flexible and adaptable framework. since each step may be determined by

different techniques according to the application domain needs. There are two main stages

in image classification using BoF; Building Vocabulary and Determining Image Class.

FIGURE 4.3: Histogram representation for BoF

For building vocabulary, large feature descriptors are clustered by applying the clustering

algorithm, such as K-Means technique to generate a visual vocabulary [208].

The BoF model can be formulated as:

Training dataset S is represented by

S = s1, s2,…,sn (4.4)

Where, s is the extracted visual features.

Using clustering algorithm like K-Means, visual words, W is represented by

W = w1, w2,...,wv (4.5)

Where v is the cluster number.

Then, the data is summarized in a v x n occurrence table of counts

Nij = n(wi, sj) (4.6)


71

Wheren (wi, sj) denotes how often the word wi is occurred in an image sj.

To determining the class of the query image the descriptor of query image is matched with

the descriptors using nearest neighbour approach.

FIGURE 4.4: Classification flow using SURF features with BoF

4.2.3 Classification model of SURF Features with BoF

Here, initially the features of the image are extracted using the Speeded-Up Robust

Features. Then the extracted features are classified using the Bag of Features (BoF)

classifier. The outputs of the classifier are stored in the Model Data Library. During the

verification process features of the input image are extracted and classified using SVM

classifier. The determined class is displayed as an output.

Retrieval Result

Classifier

k-Means

Clustering

Bag of Words

Input Image

Description of Local

Features using SURF

Histogram Generation

Input Image


Features using SURF

Vocabulary

Training Phase Testing Phase


72


The proposed methods are simulated on Intel Core i5, 2.60 GHz processor, 64-bit

Operating System in MATLAB 2016a environment. The effectiveness of the method is

measured by applying it on standard dataset, i.e. NIST – 4 which contains 800 images of

fingerprints (200 images of each class). The selection of this dataset is for the purpose of

the comparison of the classification approaches discussed in chapter 3 and this chapter.

The experiments have been carried out using various images for testing purpose arbitrarily.

The testing images are selected by the system arbitrarily and five different testcases are

considered. In all testcases, testing images are excluded from training phase. The results of

various test cases are shown further.

(a) (b)

FIGURE 4.5: Sample images from the dataset used; a) NIST – 4, b) Captured

Fig.4.6 to Fig. 4.10 demonstrates the confusion matrices of the fingerprints with fusion

features and SVM classifier. Fig. 4.11 shows the classification rate of various testcases and

Fig. 4.12 shows the classification rate of various classes.


73

Arch 16 3 1 0 80.00

Left

Loop 2 13 2 3 65.00

Right

Loop 1 3 14 2 70.00

Whorl 0 1 1 18 90.00

Arch Left

Loop

Right

Loop Whorl

76.25%

(Over all

Accuracy)

FIGURE 4.6: Confusion matrix of Testcase1 using BoF Classifier

Arch 17 1 2 0 85.00

Left

Loop 1 15 3 1 75.00

Right

Loop 0 1 18 1 90.00

Whorl 0 1 2 17 85.00

Arch Left

Loop

Right

Loop Whorl

83.75 %

(Over all

Accuracy)



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Arch 17 2 1 0 85.00

Left

Loop 0 17 2 1 85.00

Right

Loop 0 1 18 1 90.00

Whorl 0 1 1 18 90.00

Arch Left

Loop

Right

Loop Whorl

87.50 %

(Over all

Accuracy)


Arch 18 1 0 1 90.00

Left

Loop 1 17 1 1 85.00

Right

Loop 0 1 18 1 90.00

Whorl 0 2 1 17 85.00

Arch Left

Loop

Right

Loop Whorl

87.50%

(Over all

Accuracy)



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Arch 19 1 0 0 95.00

Left

Loop 0 18 1 1 90.00

Right

Loop 1 0 18 1 90.00

Whorl 0 2 2 16 80.00

Arch Left

Loop

Right

Loop Whorl

88.75%

(Over all

Accuracy)


FIGURE 4.11: Analysis of Results of various Testcases (with BoF Classifier)

76.25 83.75

87.5 87.5 88.75

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FIGURE 4.12: Analysis of Results of various classes (with BoF Classifier)


The classification system proposed in this chapter demonstrates the use of robust features

of fingerprint images. As discussed earlier, the efficiency of each fingerprint classification

system highly depend on feature extraction method. And to make the part of feature

extraction robust, the feature vector is built using SURF features. This feature vector is

given as input the classifier.

TABLE 4.1: Comparison of average Testcase accuracy and class accuracy of the approaches used for

classification

Sr.

No. Description

LBP &

GLCM with

SVM

LBP &

GLCM with

Knn

SURF with

BoF

1 Average Testccase Accuracy 74.50% 54.75% 84.75%

2 Average Arch Class Accuracy 85.00 66.00 87.00

3 Average Left Loop Class Accuracy 60.00 56.00 80.00

4 Average Right Loop Class Accuracy 67.50 46.00 86.00

5 Average Whorl Class Accuracy 72.50 51.00 86.00

Various experiments have been performed to analyse the effectiveness of the proposed

method. The experiments have been conducted for various testcases including different

fingerprint images of various classes using BoF classification and results are analysed. It

has been observed that BoF classifier achieves classification accuracy ranges from 76.25 %

to 88.75% for various testcases. The experiments have also been conducted on various

87 80

86 86

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fingerprint classes individually. The classes of different fingerprints have been chosen

arbitrarily and results are examined. Looking at the classification accuracy, it has been

concluded that arch, right loop and whorl are most prominent class that have been

classified most correctly with the classification accuracy of 86-87 % classification

accuracy. The remaining class whorl is less correctly recognized due to the limitation of

method for proper differentiation of feature points between the classes.

Table 4.1 shows the comparison of average testcase accuracies and average class

accuracies from the methods of fingerprint classification discussed in chapter 3 and this

chapter. It is important to note that the testcases are generated by the system randomly

based the bifurcation of fingerprint images as 10% for testing and for training. Hence,

comparison is shown based on the average testcase accuracies and average class

accuracies. It is observed that the BoF classifier, used with SURF descriptor, is found

strong. Moreover there is a bigger difference in the average class accuracies in case of the

LBP & GLCM features compared to BoF classifier.

Retrieval using Scale and Rotation Invariant Robust Features

78

CHAPTER – 5

Retrieval using Scale and Rotation Invariant

Robust Features

5.1 Introduction

Extraction of discriminative and reliable features is crucial for fingerprint retrieval. The

texture found in images shows an intense segregating feature for image classification and

also for image retrieval. In spite of the fact that there does not exist a formal meaning of

surface, it can be comprehended as the essential natives in images whose spatial

conveyance makes some visual examples [149]. Accordingly, the objective of a surface

element extraction strategy is to make a feature vector that catches the image surface data

while saving its substance. By considering that the ridges and valleys of fingerprints form a

textural pattern, it is then possible to capture discriminatory information through their

textural representations. Further, the captured representations in the feature vectors can be

indexed and stored for image retrieval purposes.

Feature extraction is the component of dimensionality minimization. Principal component

analysis (PCA) is the most famous feature extraction method, which wants to find a linear

mapping to preserve the total variance. Linear discriminant analysis (LDA) is another

frequently used feature extraction method; it seeks an optimal projection space, such that

the projected patterns have the maximal inter-class distance and the minimal intra-class

distance [150].

For nonlinear cases, kernel and manifold learning frameworks have pulled in much

consideration and some attractive outcomes have been acquired. Kernel PCA (KPCA) and

kernel LDA (KLDA) are the nonlinear expansion versions of PCA and LDA respectively;

they first guide the input data into the high-dimensional space through the kernel function,

and after that perform the feature extraction method. Locality preserving projections

(LPP), locally linear embedding (LLE) and isometric feature mapping are three popular


79

manifold learning algorithms [151]. These algorithms and their extensions have been

effectively utilized as a part of many image processing works.

Retrieval is defined as given an input fingerprint filter out a subset of candidate

fingerprints for the finer matching by coarsely searching the data base. If one of the

retrieved candidates originates from the same finger as the input, the retrieval is successful

for this input fingerprint; otherwise, it is a failure. Therefore, the retrieval accuracy/error is

calculated by the percentage of the input fingerprints with retrieval success/failure [152].

As retrieval is to reduce the number of finer matching for identification, the average

percentage of the retrieved fingerprints from the data base over all input fingerprints

represents the retrieval efficiency. The performance of a retrieval technique is measured by

its accuracy and efficiency. The continuous classification retrieves fingerprints in the data

base whose features fall in a neighborhood centered at the input. It usually provides better

performance and can balance the efficiency and accuracy more easily than the exclusive

classification [153]. Here we are using Bag of Features (BoF) for generating the

vocabulary. Clusters of vocabulary words are generated using k-means clustering. Using

kNN, the rank of each training dataset image with query image is found and they are

arranged in the order of rank. We finally display top n similar images from the order of

rank as retrieved images.

The dominant local feature extractors are the Scale Invariant Feature Transform (SIFT),

and the Speeded-Up Robust Feature (SURF) [96]. Due to their robustness and their time

efficiency, SIFT and SURF have been deployed in a broad scope of applications such as

object recognition, texture recognition, and image retrieval. The proposed work uses SURF

and SIFT features of the fingerprint images for retrieval.

5.2 Methodology

Image Retrieval provides a way for search-engines to retrieve query-similar images from

abundantly available/accessible digital images based on visual image contents rather than

metadata (manual image labelling / manual image annotation) [154].

For image retrieval, there are two steps which include feature extraction and fingerprint

retrieval. For feature extraction, we are using the following techniques.

Speeded Up Robust Features (SURF)


80

Scale Invariant Feature Transform (SIFT) and

Concatenation of SURF and SIFT

Panchal et al. [157] performed experiments to compare the effect of SIFT and SURF on

images. He proposed the conclusion that the number of key points detected by SIFT is

significantly higher than SURF. The experiments are carried out for key point description

by using SIFT and SURF separately as well as the concatenation of both the features.

The Feature extraction using the SURF method is discussed in the chapter 4, section 4.2.1.

In this chapter the feature extraction using SIFT and retrieval using BoF is discussed.

5.2.1 Feature Extraction using Scale Invariant Feature Transform (SIFT)

Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching

and recognition developed by David Lowe. These descriptors as well as the related image

descriptors are used for a large number of purposes in computer vision related to point

matching between different views of a 3-D scene and view-based object recognition [156].

The SIFT descriptor is invariant to translations, rotations and scaling transformations in the

image domain and robust to moderate perspective transformations and illumination

variations. Experimentally, the SIFT descriptor has been proven to be very useful in

practice for image matching and object recognition under real-world conditions.

In its original formulation, the SIFT descriptor comprised a method for detecting interest

points from a grey-level image at which statistics of local gradient directions of image

intensities were accumulated to give a summarizing description of the local image

structures in a local neighborhood around each interest point, with the intention that this

descriptor should be used for matching corresponding interest points between different

images.

Later, the SIFT descriptor has also been applied at dense grids which have been shown to

lead to better performance for tasks such as object categorization, texture classification,

image alignment and biometrics . The SIFT descriptor has also been extended from grey-

level to colour images and from 2-D spatial images to 2+1-D spatio-temporal video.


81

Generating the features of the image

The SIFT algorithm extracts distinctive features of local image patches which are invariant

to image scale and are robust to changes in noise, illumination, distortion and viewpoint. It

consists of four basic steps. Scale-space extrema estimation, key point localization, key-

point orientation assignment and descriptor generation [156].

1. Extrema detection based on Scale-space

The first step of the computation searches for extrema over all scales and image locations

[157]. Let ),( yxI be an input image, then the scale space of the image I is defined as given

below:

),(*),,(),,( yxIyxGyxL (5.1)

where * is the convolution operation in x and y directions, and the Gaussian function

2

22

2

22

1),,(

yx

eyxG

(5.2)

where σ denotes the factor of the scale space. In order to efficiently detect potential interest

points that are invariant to scale and orientation, which are also called key points in SIFT

framework, the method used the scale-space extrema in the difference-of-Gaussian (DoG)

function convolved with the image, D(x,y,σ), which can be computed from the difference

of two nearby scales separated by a constant multiplicative factor k:

),,(),,(

),(*)],,(),,([),,(

yxLkyxL

yxIyxGkyxGyxD

(5.3)

The convolved images are grouped by octave, and an octave corresponds to doubling the

value of σ. Then the value of k is selected so that a fixed number of blurred images per

octave are obtained. This also ensures the same numbers of DoG images are generated per

octave. Once DoG images have been obtained, keypoints are identified as local minima or

maxima of the DoG images across scales. This is done by comparing each pixel in the

DoG images to its eight neighbors at the same scale and nine corresponding neighboring

pixels in each of the neighboring scales. If the pixel value is the maximum or minimum

among all compared pixels, it is selected as a candidate key point.


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2. Key point localization

Scale-space extrema detection produces too many key point candidates, including some

unstable ones. To retain only stable key points, key points are filtered in this step. Once a

key point candidate has been found by comparing a pixel to its neighbors, next is to

perform a detailed fit to the nearby data for accurate location, scale, and ratio of principal

curvatures. This information allows points to be rejected that have low contrast (and are

therefore sensitive to noise) or are poorly localized along an edge [158].

FIGURE 5.1: Key point localization at different scales

3. Orientation assignment

This is the key step to achieve invariance to image rotation, in which each key point is

assigned one or more orientations based on local image gradient directions [159]. The key

point orientation is calculated from an orientation histogram of local gradients from the

closest smoothed image . For each image sample at the key point‘s scale σ, the gradient

magnitude and orientation is computed using pixel differences, let

),,1(),,1(1 yxLyxLL (5.5)

),1,(),1,(2 yxLyxLL (5.6)

2

2

2

1),( LLyxm (5.7)

)/arctan(),( 12 LLyx (5.8)


83

An orientation histogram with 36 bins, with each bin covering 10 degrees, is formed from

the gradient orientations of sample points within a region around the key point. Then the

maximum orientation is assigned to this key point; additional key points will be created

with orientation which is within 80% of the maximum orientation.

FIGURE 5.2: Orientation Histogram

4 Key point descriptor

The previous operations have assigned an image location, scale, and orientation to each

key point, which ensure the invariance to image rotation, location and scale. And then we

want to compute descriptor vectors for each key point such that descriptors are distinctive

and robust to other variations, such as illuminations etc. Compute the feature descriptor as

a set of orientation histograms on 4 x 4 pixel neighborhoods.

The orientation histograms are relative to the key point orientation and the orientation data

comes from the Gaussian image closest in scale to the key point's scale. Histograms

contain 8 bins each, and each descriptor contains a 4 x 4 array of 16 histograms around the

key point. This leads to a SIFT feature vector with (4 x 4 x 8) 128 elements [160].

FIGURE 5.3: Computation of Key point Descriptors


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5.2.2 Feature Extraction using SURF and SIFT

The features of the input image are extracted by applying SURF, SIFT and the

concatenation of both the features. The outputs are stored in the model data library. During

the image retrieval, by applying SURF, SIFT and the concatenation, the features of the

input image are extracted and verified with the stored data. In the verification process the

features are checked whether it is matched or not. Then ranking process is carried out and

the result is displayed.

5.2.3 Retrieval using Bag of Features

Content Based Image Retrieval (CBIR) systems are used to find images that are visually

similar to a query image. The application of CBIR systems can be found in many areas

such as a web-based product search, surveillance, and visual place identification. A

common technique used to implement a CBIR system is bag of visual words, also known

as bag of features. Bag of features is a technique adapted to image retrieval from the world

of document retrieval [161].

Instead of using actual words as in document retrieval, bag of features uses image features

as the visual words that describe an image. Image features are an important part of CBIR

systems. These image features are used to gauge similarity between images and can

include global image features such as color, texture, and shape. Image features can also be

local image features such as speeded up robust features (SURF), histogram of gradients

(HOG), or local binary patterns (LBP) [162].

The benefit of the bag-of-features approach is that the type of features used to create the

visual word vocabulary can be customized to fit the application. The BoW model is first

used in the areas of natural language processing and information retrieval, in which a

document is represented as a bag of orderless words. In multimedia search area, the BoW

model is adopted to represent an image as a collection of orderless visual words, where one

key issue is how to construct a visual vocabulary.

In essence, the construction of visual vocabulary is to vector-quantize the local image

descriptors in a training set. The speed and efficiency of image search is also important in

CBIR systems. For example, it may be acceptable to perform a brute force search in a

small collection of images of less than a 100 images, where features from the query image


85

are compared to features from each image in the collection. For larger collections, a brute

force search is not feasible and more efficient search techniques must be used [163].

The bag of features provides a concise encoding scheme to represent a large collection of

images using a sparse set of visual word histograms. This enables compact storage and

efficient search through an inverted index data structure. The Computer Vision System

provides a customizable bag-of-features framework to implement an image retrieval

system. The following steps outline the procedure:

Select the Image Features for Retrieval

Create a Bag Of Features

Index the Images

Search for Similar Images

In recent past, Bag-of-Features (BoF) model is getting popularity in the area of image

retrieval, also known as object retrieval, based on local feature, due to the scalability of

retrieval system. In this model, local features are trained to build visual vocabulary in

advance. This vocabulary is then used to quantify the local features of image. the similar

local features are presented in approximation as teir cluster centre, as visual word. The

quantization of visual word affects the retrieval result strongly in the image retrieval based

on local features. Moreover, there is a crucial effect of pre-trained bag-of-features on the

quantization of visual word.

In BoF based image retrieval system, to improve the visual word training, a k-means

clustering algorithm is used. The distribution of features data on each dimension is

analysed and the distance method, which is used to partition the data space in high-

dimensional indexing according to the data distribution adaptively, is combined to obtain

the initial clustering centres. Finally the visual vocabulary is obtained by using k-means

algorithm to cluster the feature data and train the visual words.

Image Retrieval System

The main procedures of image retrieval based on bag of words model include two steps,

building index table and retrieval. During building index table, images in database are

transformed to sets of visual words, and are presented by visual words in corresponding

database. In retrieval process, the query image is transformed to a set of visual words at

first, and each visual word is used to find out relative indexed images in inverted index


86

table [165].

A generalized diagram of image retrieval using bag of features is shown in Fig. 5.4. After

obtaining the visual vocabulary, the corpus can be acquired by quantifying each image in

database. The vector space model in information retrieval can be used to retrieval. In this

model, the query image and each image in database are expressed to be sparse vector of

visual words.

FIGURE 5.4: Image Retrieval on the basis of Bag of Words

The major process of transform from image to visual words includes the following steps.


87

Detection features and description of features

SIFT and SURF features are extracted from each image in image database to describe local

features. SIFT has robust invariance to image‘s scale transform, rotation transform and

luminance transform. The extraction processes are described in previous sections.

Training the Visual Vocabulary

In BoF model, the training of visual vocabulary is an important step. This step aims to

obtain a space division of features description vector. In the phase of feature quantization

phase, feature description vectors which are falling into the same space division should be

treated as the same visual word. Then they are matched while image matching is

performed in image retrieval. The quantization error of visual word is strongly determined

by the correctness of space division and hence, the performance of visual vocabulary

ultimately affects the image retrieval accuracy.

Quantization of features

Quantization of features is the process of assigning one feature to one or multiple visual

words. To assign an image features to visual word(s), the common way is to search for

nearest neighbors among the vocabulary obtained in the vocabulary generation step.

Finally the visual word ID is used to index file is generated.

For the indexing in the database, the quantization characteristics of the image the nearest

neighbor search method in the visual vocabulary of the nearest cluster centre away from

the feature vector is used for a given localized feature of each image. corresponding to the

visual word of the local image characteristics, index listing is performed.

K-Means Clustering

Fingerprints in the information base are bunched to encourage a quick recovery process

that maintains a strategic distance from comprehensive examinations of an info unique

mark with all fingerprints in the information base [166].

k-means is one of the simplest unsupervised learning algorithms that solve the well-

known clustering problem. The procedure follows a simple and easy way to classify a

given data set through a certain number of clusters (assume k clusters) fixed apriori. The

main idea is to define k centers, one for each cluster. These centers should be placed in a


88

cunning way because of different location causes different result. So, the better choice is to

place them as much as possible far away from each other. The next step is to take each

point belonging to a given data set and associate it to the nearest center. When no point is

pending, the first step is completed and an early group age is done.

At this point we need to re-calculate k new centroids as center of the clusters resulting

from the previous step. After we have these k new centroids, a new binding has to be done

between the same data set points and the nearest new center. A loop has been generated.

As a result of this loop we may notice that the k centers change their location step by step

until no more changes are done or in other words centers do not move any more. Finally,

this algorithm aims at minimizing an objective function knows as squared error function

given by:

c

i

c

j

ji

i

vxVJ1 1

2||)(||)(

(5.9)

where,‗||xi - vj||‘ represents the Euclidean distance between the xi and vj.

‗ci‘ denotes the number of vocabularies in ith

cluster.

‗c‘ shows the number of cluster centers.

A widely used construction scheme is to perform exact k-means clustering and treat each

cluster centroid as a visual word. In the word assignment stage, a new local descriptor is

mapped to a visual word by searching its nearest cluster centroid.

Although this method is quite effective, its time and space complexity is extremely high,

making it even unpractical for handling a large set of training samples. To improve the

computational efficiency, a number of methods have been designed to efficiently construct

visual vocabularies. An approximate k-means clustering scheme speeds up the vocabulary

construction by replacing the exact computation with an approximate nearest-neighbor

method.


89

FIGURE 5.5: Example for k-Means Clustering with 4 clusters

Fig. 5.5 represents an example for k-Means Clustering with 4 clusters. The vocabularies

are splitted and clustered into 4 clusters. The four clusters are represented using different

colors such as red, blue, green and black.

Image Ranking using k-NN

The algorithm shows the steps of our decision-making strategy for test instances. Given a

test instance x, we begin by finding its k-nearest neighbors. Let },....,1|),({ kjjxN k be

the k-closest neighbors of the example x. Nk(x, j) is the j-th closest element neighbor of the

occurrence x. At that point, we abuse an overall positioning model to re-rank the k-closest

neighbors.

Let },....,1|),({ ' kjjxN k be the re-ranked neighbors of the instance x. ),(' jxN k is the j-

th trustable neighbor of the instance x, decided by the ranking model. Finally, we apply a

weighted voting strategy to obtain the final prediction. The ranking model used to re-rank

the k-nearest neighbors based on their trustiness explains how to determine the weights for

the weighted voting strategy [167].

The training examples are vectors in a multidimensional feature space, each with a class

label. The training phase of the algorithm consists only of storing the feature vectors and

class labels of the training samples.


90

In the classification phase, k is a user-defined constant, and an unlabeled vector (a query or

test point) is classified by assigning the label which is most frequent among the k training

samples nearest to that query point. A commonly used distance metric for continuous

variables is Euclidean distance. For discrete variables, such as for text classification,

another metric can be used, such as the overlap metric (or Hamming distance). In the

context of gene expression microarray data, for example, k-NN has also been employed

with correlation coefficients such as Pearson and Spearman. Often, the classification

accuracy of k-NN can be improved significantly if the distance metric is learned with

specialized algorithms such as Large Margin Nearest Neighbor or Neighbourhood

components analysis.

FIGURE 5.6: Retrieval Flow

A drawback of the basic "majority voting" classification occurs when the class distribution

is skewed. That is, examples of a more frequent class tend to dominate the prediction of the

new example, because they tend to be common among the k nearest neighbors due to their

large number. One way to overcome this problem is to weight the classification, taking into

Retrieval Result

Ranking using kNN

k-Means

Clustering

Bag of Words

Input Image


Features using SURF

and SIFT

Histogram Generation

Input Image


Features using SURF

and SIFT

Vocabulary

Training Phase Testing Phase


91

account the distance from the test point to each of its k nearest neighbors. The class (or

value, in regression problems) of each of the k nearest points is multiplied by a weight

proportional to the inverse of the distance from that point to the test point. Another way to

overcome skew is by abstraction in data representation. For example, in a self-organizing

map (SOM), each node is a representative (a center) of a cluster of similar points,

regardless of their density in the original training data. K-NN can then be applied to the

SOM. Fig. 5.7 shows the retrieval system diagram used for the experiments.


The proposed method is simulated on Intel Core i5, 2.60 GHz processor, 64-bit Operating

System in MATLAB 2016a environment. The effectiveness of the method is measured by

applying it on standard dataset, i.e. FVC which contains 200 images of each fingerprint

classes.

The experiments have been carried out using various images for testing purpose arbitrarily

from the datasets of FVC and captured images. The testing images are selected by

manually. Testing images are excluded from training phase. The results of various test

images are shown further. Fig. 5.7 shows the sample dataset images used. Fig. 5.8 is

showing the sample query and retrieved images. Following performance measurement

equations are used to analyse the results:

Precision (P) = True Positives / (True Positives + False Positives) (5.10)

Recall (R) = True Positives / (True Positives + Missed) (5.11)

The result analysis of FVC dataset images and captured image dataset is shown in Table

5.1 to 5.4.Table 5.1 shows the precision and recall values on FVC dataset, when SIFT,

SURF and fusion features are used without applying classification phase. Euclidean

distance is used to rank the similar images. Table 5.2 shows the precision and recall values

on FVC dataset, when SIFT, SURF and fusion features are used after applying

classification phase (as described in Chapter 3 and Chapter 4. Retrieval results are obtained

using BoF classifier to rank the similar images. Table 5.3 shows the precision and recall

values on captured image dataset, when SIFT, SURF and fusion features are used without

applying classification phase. Euclidean distance is used to rank the similar images. Table

5.4 shows the precision and recall values on captured image dataset, when SIFT, SURF


92

and fusion features are used after applying classification phase (as described in Chapter 3

and Chapter 4. Retrieval results are obtained using BoF classifier to rank the similar

images.

(a) (c) (b)

FIGURE 5.7: Sample images from the dataset used; a) FVC2002, b) FVC2004 and c) Captured

(a)

(b)

FIGURE 5.8: a) Query fingerprint image and b) retrieved similar images

Table 5.1 and 5.2 gives the experiment results on FVC dataset images. Table 5.3 and 5.4

gives the experiment results on captured image dataset. First column shows the fingerprint

image used as a query image for retrieval. Next four columns represents the results when

SURF and SIFT descriptors are used independently. Next two columns represent the

retrieval results when SURF and SIFT features are concatenated. The last two columns

represent the results of BoF classifier used with concatenated features. Table 5.1 shows the

results without using classification before retrieval phase. Table 5.2 shows the results of

retrieval after applying classification results as discussed in chapter 4.

Table 5.3 and 5.4 gives the experiment results on captured image dataset similar to Table

5.1 and 5.2.


93

TABLE 5.1: Retrieval Result analysis of FVC dataset images without performing classification phase

Dataset SURF with BoF SIFT with BoF Concatenation Bag of Features

Precision Recall Precision Recall Precision Recall Precision Recall

101_1 0.40 0.57 0.40 0.57 0.60 0.86 0.50 0.71

102_2 0.50 0.71 0.50 0.71 0.50 0.71 0.50 0.71

103_3 0.40 0.57 0.40 0.57 0.50 0.71 0.40 0.57

104_4 0.30 0.43 0.40 0.57 0.20 0.29 0.40 0.57

105_5 0.40 0.57 0.30 0.43 0.50 0.71 0.60 0.86

106_6 0.50 0.71 0.60 0.86 0.40 0.57 0.30 0.43

107_7 0.30 0.43 0.30 0.43 0.30 0.43 0.40 0.57

108_8 0.40 0.57 0.40 0.57 0.50 0.71 0.60 0.86

TABLE 5.2: Retrieval Result analysis of FVC dataset images after performing classification phase



101_1 0.50 0.71 0.60 0.86 0.60 0.86 0.70 1.00

102_2 0.50 0.71 0.60 0.86 0.60 0.86 0.50 0.71

103_3 0.50 0.71 0.70 1.00 0.50 0.71 0.60 0.86

104_4 0.60 0.86 0.30 0.43 0.40 0.57 0.40 0.57

105_5 0.30 0.43 0.40 0.57 0.50 0.71 0.70 1.00

106_6 0.50 0.71 0.50 0.71 0.60 0.86 0.60 0.86

107_7 0.60 0.86 0.60 0.86 0.50 0.71 0.70 1.00

108_8 0.60 0.86 0.50 0.71 0.60 0.86 0.60 0.86

TABLE 5.3: Retrieval Result analysis of Captured Image dataset images without performing classification

phase



finger163_2 0.20 0.67 0.20 0.67 0.20 0.67 0.20 0.67

finger18_3 0.10 0.33 0.20 0.67 0.20 0.67 0.20 0.67

finger1_1 0.20 0.67 0.10 0.33 0.20 0.67 0.20 0.67

finger27_1 0.10 0.33 0.10 0.33 0.10 0.33 0.20 0.67


94

TABLE 5.4: Retrieval Result analysis of Captured Image dataset images after performing classification phase



finger163_2 0.20 0.67 0.20 0.67 0.20 0.67 0.20 0.67

finger18_3 0.20 0.67 0.20 0.67 0.20 0.67 0.20 0.67

finger1_1 0.20 0.67 0.10 0.33 0.30 1.00 0.30 1.00

finger27_1 0.10 0.33 0.10 0.33 0.10 0.33 0.20 0.67


The retrieval system proposed in this chapter demonstrates the use of fusion features using

robust SURF and scale and rotation invariant SIFT features. The feature vector, generated

by processing training images is given as an input to the BoF classifier to rank the

similarity of dataset images and n most similar images are displayed as a result.

Experiments are carried out on the FVC fingerprint dataset and captured image dataset to

analyse the effectiveness of the proposed method. FVC dataset if mainly used for the

purpose of fingerprint verification. As it contains eight image of each human finger, it is

suitable for evaluating fingerprint retrieval. Fingerprint images are arbitrarily considered

for testing. The test-set of the images is excluded from training-set. FVC dataset used for

the experiments, contains total 352 fingerprint image. The another dataset of captured

images contains four images of one human finger. This dataset used for the experiments,

contains total 366 fingerprint images. The result analysis is done by displaying minimum n

(n=10) most similar images as a retrieval result. As, the number of images per fingerprint

is less than minimum similar images to be displayed, recall measurement features the

actual efficiency of the system. It is observed from the experiments that the retrieval results

(obtained from Eq. 5.11) contains more than 50% of recall value in almost all cases when

concatenated features are used with BoF classifier for calculating similarity between the

query and dataset images in both the kind of datasets. This recall value is found better than

the few fingerprint retrieval systems presented in the literature [213].

Conclusion and Further Directions

95

Chapter 6


6.1 Conclusion

Fingerprint image retrieval system primarily involves two steps, namely, fingerprint image

classification and retrieval. As a first step, the fingerprint image is classified in to one of

the four standard fingerprint image classes; Arch, Left loop, Right loop and Whorl. The

classification goes through two basic steps; feature extraction and applying classifier. The

proposed first method extracts the texture features using LBP and GLCM and uses

concatenation of both these features for further classification. Two standard classifiers;

SVM and kNN is used to judge the improvement in the classification. This approach is

achieving the classification accuracy ranging from 68.75% to 82.50% on various testcases

tried, when the features are applied to SVM classifier, which higher than the classification

accuracy achieved using kNN classifier. From the analysis, arch are most prominent class

that have been classified most correctly with 85% classification accuracy using SVM

classifier.

The robust features, for improving the classification accuracy have been explored in

second proposed classification technique. A feature vector is generated consisting of SURF

features from the training images and has been exploited as an input to the BoF classifier.

It has been observed that BoF classifier achieves classification accuracy ranges from 76.25

% to 88.75 % for various testcases. From the analysis, arch, right loop and whorl are most

prominent class that have been classified most correctly with the classification accuracy of

86-87 % classification accuracy.

The classified query image is given as query to the retrieval system, which tries to search

and display n similar images from the same class as of the query image. The retrieval

approach discussed in this work, uses the concatenated features from SURF and SIFT

features to make the features scale and rotation invariant. Precision and recall are the two

performance measures used to analyze the efficiency of the retrieval system. Due to the


96

number of images of one fingerprint is finite, the recall is considered as the major factor

for evaluating the performance of the proposed system. From the results it is observed that

the efficiency of the system in which retrieval is performed after classification, is giving

good recall value.

6.2 Further Directions

Fingerprint retrieval research presented here can be further advanced as:

Proposed fingerprint image classification and retrieval methods can be used for the

applications of recognition and registration on fingerprint images.

The use of more realistic fingerprint image databases in which we expect that the

retrieval effectiveness of our image descriptors will be considerably improved.

References

97

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Publications

111

List of Publications

[1] Sudhir Vegad, Dr. Tanmay Pawar, "Fingerprint Image Retrieval: A Novel Approach‖,

Springer International Conference on Intelligent Systems and Signal Processing,

ISSP-2017, March 2017

[2] Sudhir Vegad, Dr. Tanmay Pawar, ―Fingerprint Image Retrieval using Statistical

Methods‖, International Journal of Computer Science and Information Security, ISSN

– 1947 5500, Vol. 15 (11), pp 246-249, November 2017

fingerprint image classification and retrieval using...

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