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CHAPTER 2

LITERATURE SURVEY

Presently biometricverification systems are extensivelyused since theses offers inherent

advantages above classical information based and token based personal recognition

approaches. This Chapter of the thesis describes the survey related to the unimodal

biometrics of palm namely palmprint and fingerprint along with multimodal biometric

fusions used for personal identification or system authentication.

2.1 Literature review of palmprint as unimodal biometrics

Palmprint has demonstrated to be unique and stable biometric individuality. It led to the

expansion of products with palmprints as biometric features and their use in a number of

real applications. Roughly all palmprint recognition techniques that are currently

availablecapture the 2D image of the palm and useit for extracting the features and

matching. Even though 2D palmprint recognition can attain high accuracy, it is very easy to

counterfeit the 2D palmprint images.In depth 3D information is also lost in the imaging

process.Since last decade; palmprint has been used as biometricmodality. These palmprint

recognition systems uses encoding and matching of creases, it is found that these systems

are not reliable like the systems which use ridges. It restricts the palmprint usage application

in large scale person identification where the distinctiveness and insensitivity of the

biometric modality required with the change in skin conditions and age.In recent time, to fill

gap, researchers have proposed many algorithms for palmprint matching which uses

ridges.These systems uses simple fingerprint matching algorithm. These systems use

multiple features for matching purpose and also establishes reliable orientation field.

However the palmprint features are different than the fingerprint in many aspects: 1)

palmprints are larger than fingerprints and hence contains more numbers of minutias, 2)

deformability of palms is more as compared to fingertips, and 3) the discrimination power

and quality for different palm regions vary considerably.

Hence these matchers can are not handle the noise and distortionproperly despite of heavy

computational cost.However due to the vulnerability of biometric systems to replay, brute

force attacks and database , these attacks need to be analyzed before massively deploying

biometric systems in security systems.

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The popularity of palmprint as a biometric and availability of literature in the field, owes to

its various advantages and hence many researchers contribute in this area. N. Duta [4]

presented a survey on hand shape based biometric systems. The survey consists of review of

component modules including the employed algorithms, system taxonomies, performance

evaluation methodologies, summary of the accuracy results reported in the literature, testing

issues, commercial hand shape biometric systems, successful deployments and US

government evaluations. They have also mentioned few limitations of the hand shape

biometric and gave some directions for future research. A. Kong et al [5] gave an overview

of palmprint investigationfor particular the devices used for capturing palmprints, required

preprocessing, different verification algorithms and fusion algorithms for large databases

along with measures for protecting the real time palmprint identification systems and

privacy of the users followed by some suggestions.

D. Zhang et al [6] presented online personal identification using low resolution palmprint

images effectively. A device for acquiring online palmprint images and fast palmprint

recognition algorithm make up the system. The system has been implemented for real time

personal identification based on CCD camera to capture the palmprint images. The image

alignment for feature extraction has been performed with the help of a robust image

coordinate system followed by preprocessing, feature extraction, representation and

matching. The algorithm used for preprocessing extracts the central area from a palmprint

image. The texture features of palmprint are extracted using 2-D Gabor phase coding.

Normalized hamming distance has been used for matching measurement. Genuine

acceptance rate of 98 percent and false acceptance rate of 0.04 percent has been achieved

with equal error rate of 0.6 percent on palmprint database produced of 7,752 palmprint

images from 386 different palms. A. Kumar et al [7] investigated an approach to enumerate

the quality of sensed data from the user templates to incorporate the quality of sensed data

for generating a reliable estimation for the matching scores. Extraction of user quality

depends on the confidence of generating reliable matching scores from the user templates.

Simultaneous extraction of palmprint and hand-shape images from the single hand image

has been conducted to ascertain the performance improvement for the individual trait.

Quality of biometric measurement also depends on the image quality, which is often linked

to imaging resolution. Finger knuckles have also been employed for experimentation.

Results presented in the paper show significant improvement in performance while

incorporating the method of user quality in the matching stages. The user quality based

fusion of the two biometric modalities also achieved promising improvement in

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performance.C.Han et al [8] proposed a personal authentication system based on scanner

consisting of enrollment and verification stages using palmprint features suitable in many

network based applications. Training samples have been collected and passed through

preprocessing, feature extraction and modeling modules to generate the matching templates

during the enrollment phase. During verification phase a query sample has been processed

by the preprocessing and feature extraction modules followed by matching with the

reference templates from the stored database. The preprocessing module extracts the region

of interest for each sample through image threshold, border tracing and wavelet based

segmentation. The palmprint features have been extracted from this region using Sobel and

morphological operators. Modeling module generates the reference templates of specific

user. Finally back propagation neural network and the linear correlation function have been

used for the template matching and to measure the similarity during the verification phase.

Experimental results verify the validity of the approaches in personal authentication by

obtaining accuracy rate more than 98% with FAR and FRR values below 2%. C. Lin et al

[9] presented reliable and robust personal verification approach using palmprint features

that does not require prior knowledge about the objects and the parameters can be set

automatically. They have adopted a flatbed scanner for capturing palmprint images that

work without palm inking or a docking device. Two finger webs selected automatically as

the datum points define the region of interest in the palmprint image. From this region the

main palmprint features that include directional information and multiresolution

decompositions have been extracted by applying hierarchical decomposition mechanism at

different resolutions. Validity of the palmprint verification approach have been verified on

4800 palmprint images collected from 160 persons with FRR as 0.75% and FAR equal to

0.69%. The training and on-site image taking habits are consistent. D. Hu et al [10]

proposed an algorithm for extracting the proper features from image environment based on

locality conserving criterion using two dimensional locality preserving projections. The

algorithm works directly on the image matrix of images and the dimensionality of the bases

derived for image representation is much smaller, which means a more accurate

approximation of the original image. Experimental results on PolyU palmprint images show

the effectiveness of the method. K. Tiwari et al [11] present a palmprint based automatic

recognition system that uses local structure tensor for extracting features from the

palmprint. Features have been acquired from an enhanced palmprint image by dividing the

image into sub-images, based on local properties within the sub-image. To emphasize the

texture of the palmprint, force field transformation has been used and the chosen dominant

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orientation pixels have been used for feature extraction to reduce the effect of noise. A

tensor matrix for the sub-image by averaging structure tensor values of the dominant

orientation pixels within a sub-image. Feature matrix has been generated by Eigen

decomposition of each tensor matrix. Matching decision has been made using Euclidean

distance between feature matrices of two palmprints. The proposed scheme has been tested

on 549 images of IITK database, 5238 images ofCASIA database and 7752 images of

PolyU database. It has been found that the structure with kernel value k=5 for filtering

achieved high accuracy for these databases and performed well again than the two top

known systems [6, 12].

M. Adan et al [13] present a hand biometric system for verification and recognition

purposes based on three keys. 1) The system is based on a natural reference system defined

on natural layout of the hand and thus no hand-pose training or a pre-fixed position required

in the registration process. 2) The features of hand are obtained through the polar

representation of the contour of hand implying minimum image processing and low

computational cost. 3) Right and left hands are used instead of common methods that use

one hand allowing consideration of distance measures for direct (R/R, L/L) and crossed

(R/L, L/R) hands obtaining improvements in identification ratios,false rejection rate and

false acceptance rate. The experimentation is performed on 5640 images belonging to 470

users providing results good enough for future security/control applications.G.Badrinath et

al [14] presented a palmprint based human recognition scheme which uses the palmprint

extraction from the obtained hand image. The extracted palmprintis enhanced for correction

of difference in brightness.Histogram utilization and coarse estimation of reflection can

improve the texture of palmprint.The improved palmprint is then divided into overlapping

square blocks.Principle component analysis reconstruction error has been used to categorize

the blocks into non-palmprint block or good block.The phase difference of vertical and

horizontal phase from each good block has been used as the palmprint features.

Hamming distance has been used for computation fo matching score for features of live

palmprint and corresponding good bloks of enrolled palmprint. The weighted sum rule in

which weights based on the average discriminating level has been used for the fusion of the

score obtained from all good blocks. 549 images from IITK database, 5239 hand images

from CASIA and 7752 hand images from PolyU database has been used for analyzing the

system performance. For all datasets theresults acheived Correct Recgnition Rate of 100

percent and a low Equal Error Rate.Robust system performence found till the noise level is

less than 50% and 64 % for good region and the bad region respectively.M. Aykut et al [15]

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propose active appearance model for robust palm segmentation that eliminates the

requirement of the whole hand image appearing in the scene. The model has ability to

efficiently perform palm segmentation on the cluttered backgrounds and accurate decision

making on whether object in the scene is a palm. Performance evaluation is based on two

metrics namely modified point-to-curve distance and a margin width. Experimental results

exhibit outcomes like efficient segmentation of the palm area from the complex

background, successfully determine the hand objects because of the texture matching

property, palm model fitting for identifying hand object in the scene, effective fitting results

of the proposed palm model; at the 768 × 576 image resolution, average fitting error is

approximately 3 pixels.

J. Doi et al [16] proposed a personal authentication method using noncontact real-time

feature extraction. The feature extraction of the palm flexion creases and finger geometry

are integrated into a small number of discrete points based on the anatomy. A video camera

acquires image of palm placed freely facing toward the camera. In order to eliminate any

constraints, the fingers are brought together and the palm is straightened out. Intersection

points of the three finger flexion creases on the four finger skeletal lines form the discrete

feature points for the fingers. Intersection points of the major palm flexion creases and

prominent creases of the palm on the extended finger skeletal lines form the feature points

for the palm. The orientations of the creases at the intersection points are also extracted as

the features and used for matching purpose. Personalized threshold values and additional

parameter comparisons are applicable for a more reliable matching. The enrollment is as

simple as to acquire the palm image for the new image. The high quality of newly captured

image causes the replacement of the previously acquired image. X. Wu et al [17] used

stable line features of palm included thewrinkles and principal lines to clearly explain a

palm print in low-resolution images. The palm line extraction has been achieved through a

set of directional line detectors used for extracting these lines in dissimilar directions. These

uneven lines are characterized using their chain code in order for avoiding the loss of details

of the palm line organization. For matching the palm lines, a matching score is defined

among two palm prints based on the points of their palm lines. The investigational results

carried out on general database (DB1) for palm print verification and performance has been

compared with the 2-D Gabor algorithm and the Sobel method. Another experiment for

testing robustness against dirty palms (DB2 and DB3) confirms the advantage over the 2-D

Gabor algorithm. The speed and storage required for a real-time biometric schemecan be

satisfied.Biometric identity is also verified with palmer flexion crease recognition. T.

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Cooket al [18] proposed a method of automated flexion crease recognition that can be used

to identify palmer flexion creases in online palmprint images. To extract the flexion creases,

a modified image seams algorithm has been used and a matching algorithm using k d-tree

nearest neighbor searching has been used to calculate the similarity between them.

Experimental results on 1000 images from 100 palms show that when compared to

manually identified flexion creases, a genuine acceptance rate of 100% has been achieved

with 0.0045% as false acceptance rate. O. Nibouche et al [19] suggest a palmprint matching

system based on the extraction of feature points. The feature points are corners formed by

the intersection of creases and lines as shown by a scale-space representation. Such points

can still be extracted even on low resolution palmprints, unlike minutiae. Matching has been

performed using an SVD factorization of a proximity matrix taking in account the

coordinates of the detected points and their local texture. The experiments have evidenced

an EER of 0.10%. N. Duta et al [20]investigate the practicality of individual identification

in light of feature focuses separated from palmprint images. A set of feature points along the

prominent palm lines, and the associated line orientation, has been extracted from a given

palmprint image. Matching scores between the corresponding sets of feature points of the

two palmprints have been used to decide if two palmprints belong to the same hand. Point

matching technique that takes into account the nonlinear deformations as well as the outlier

points present in the two sets aids in matching the two sets of feature points/orientations.

The discrimination power of palmprints has been exhibited by the estimates of the matching

score distributions for the genuine and imposter sets of palm pairs. Overlap between

genuine and imposter distributions has been found to be about 5%. The overlap has been

due to the noise present in the palm images or other non-linear deformation. With addition

of more subjects, one expects some overlap due to similar palms of different persons.

Adding palmprint information may improve the identity verification provided by

fingerprints in cases where fingerprint images cannot be properly captured e.g. due to dry

skin.

Palmprint recognition became a demanding problem due to large nonlinear distortion, low

superiority of the patterns and the calculationcomplexity for the big image size. R. Cappelli

et al [21] introduced high resolution palmprint recognition scheme using minutiae that deal

with these issues. Though the system follows the typical sequence of steps used in

fingerprint recognition, care has been taken to specifically design and optimize each step to

process large palmprint images with a good tradeoff between accuracy and speed. Reliable

detection of minutiae has been extracted using series of steps for robust feature extraction.

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The matching algorithm found to beextremely efficient and robust to skin distortion as it is

based on a local matching policy and anproficient and compressedillustration of the

minutiae. Investigational results on the database (THUPALMLAB)shows that the scheme

compares very positively with state-of-the-artschemes. With complex and open working

environment, low-quality images are playing a major role in real life biometric

identification systems. The crucial component of low quality is low resolution. C. Su [22]

uses the object extraction method to extractindex, thumb, ring, middle and small fingers

from the hand images. The algorithm finds the exact locations of thefinger to finger

valleysand fingertips. Many useful geometry features are contained in the extracted fingers

that can be used for person identification. For image comparison, the geometry features are

transferred by using descriptor to another feature-domain. Scaling and magnification of

finger image is performed to obtain more salient feature. Subtraction of image is used to

examine the difference between the two images. The small finger has false acceptance rate

of 1% while false rejection rate is 1%. The thumb finger has false acceptance rate of 1% and

false rejection rate is 5%. The palm image has false acceptance rate of 5% while false reject

rate is 7%. C. Su [23] uses an extracting technique to obtain palm images. This algorithm

determines the precise locations of finger to finger valleys and fingertips and on a hand.

Palm images can be extracted after locating these positions. Such images contain many

useful geometrical features that can be used to identify palms. Orientations and centroid of

palms are identified in the algorithm. To align different palms to the same position for

geometrical comparison, image rotation, shifting and interpolation techniques are used. It is

observed that the overall error rate was below 3% for the entire experimental results. J. Wu

et al [24] proposed improved regional content feature and hand geometry based technique

for low resolution hand images with croping th ROI. At coarse level it has added the angle

information to the line based hand geometry. A simple sequence labeling segmentation

method has been developed that chooses conditional regions which are comparatively

steady in segmentation through constraint of region area; Assuming that at fine level, the

gray level changing rate is presented by gradient value of each pixel. From conditional

regions the low average gray-levels have been selected sincedense textures and distinctive

lines foreverdisplay lower gray-levels than their surrounding areas. Feature vectors are the

regional centroid coordinates. For measuring distances between feature vectors with

different dimensions, regional spatial relationship matrix has been built up. The method

utilizes more information available in low-resolution hand images compared to region of

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interest based state-of-the-art algorithms. Also, method has been effective and robust to

rotation and translation.

A. Kumar et al [25] present an approach for personal authentication using hand images.

Performance of verification system based on palmprint has been achieved by integrating

features of hand geometry. A single camera has been employed to simultaneously acquire

palmprint and hand geometry images, avoiding inconvenience of using two different

sensors. Salient features of palmprint and hand shape images have been extracted and their

combined and individual performance has been examined. Simple image acquisition setup

without special illumination and avoiding use of alignment pegs are the advantages of the

method. Database consisting images of 100 users achieves promising results suggesting that

the fusion of matching scores can achieve better performance than the fusion at

representation. J. Guo et al [26] present an approach using infrared illumination device for

personal identification using hand geometrical features. The system can be widely used

under dark environment and complex background scenarios. The users can place their hand

freely in front of the camera without any pegs or templates. Total 13 important points have

been detected from a palm image and 34 features calculated from these points for further

recognition; thus achieving better detection accuracy. The averaged Correct Identification

Rate is 96.23% and averaged False Accept Rate is 1.85%. The results prove that the contact

free system can be considered as an effective practical identity verification system.

Though the technology of hand shape recognition has existed for about last three decades

most of the earlier works aimed to implement fast, low cost and practical systems where

fixed positioning guides have been used to facilitate the placements of hands and fingers.

These guides also serve as reference points to reduce the deformability of the shapes of

hands and fingers and to assist the extraction of features of the hands. Yet, since the hands

are highly articulated, there is still variance of the measured features even if the guides are

used, which results in low-medium positive recognition accuracy. Furthermore, position

fixed guides are not suitable for all hands of different dimensions. W. Xiong et al [27]

addressed the problem of deformable hand shape recognition in biometric systems without

any positioning aids like pegs. Separation and recognition of multiple rigid fingers has been

achieved under Euclidean transformations. Representation and optimal alignments of the

fingers has been done using an elliptical model. Similarity measure during alignment search

based on finger width measurements defined at nodes by controllable intervals achieves

balanceable recognition accuracy and computational cost. The method bridges the

traditional handcrafted feature methods and the shape distance methods. R. Hu et al [28]

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propose a recognition technique for hand which is named as Coherent Distance Shape

Contexts. This method that is in light of two traditional shape representations specifically

Shape Contexts and Inner-separation Shape Contexts. Coherent Distance Shape Contexts

has great capacity to catch discriminative features from hand shape and manage the vague

correspondence issue of hand landmark points. It can extract features from the curve of

fingers. It is very robust to different poses of hand or elastic deformations of finger valleys.

To check the efficacy of Coherent Distance Shape Contexts, a hand picture database

containing 4000 grayscale left hand pictures of 200 subjects has been made, on which

Coherent Distance Shape Contexts has attained to the precise distinguishing proof rate of

99.60% for identification and the Equal Error Rate of 0.9% for confirmation that are

equivalent with best in class hand shape acknowledgment systems.J. Wang et al [29]

propose compact hand extraction to assist in computerized hand shape recognition. Initially,

an image enhancement technique based on singular value decomposition has been devised

to remove dark backgrounds by reserving the skin color pixels of image of hand. The

polynomial approximation YCbCr color model has been then used to extract the hand.

Lighting compensation has been applied after alignment to the adaptable singular value

decomposition. A hierarchical pyramid sampling algorithm has been finally used to reduce

the impact of variations in hand shape. A self-Eigen hand recognizer with genetic

algorithms (GA) has been constructed for selecting discriminant eigenvector subsets for

classification. The work maximizes the differences in hand images for various hand shapes

and also minimizes variations in lighting and pose for same hand shape. The method

applied to images from own database and a live sequence, performs more efficiently than

conventional ones that do not use compact hand extraction against complex scenes. The

classification system achieved an AAR of 99.55% and an FAR of 0.0001% for 768 images

included in inside testing. Live testing achieved classification accuracy rate of 91.7%, with

an error rate of 8.3%. Using an AMD64 Athlon CPU 2.0 GHz personal computer for images

size of 160×120 pixels, the speed up achieved is less than 1 s per hand shape.

Image alignment is an important step in palmprint recognition. W. Li et al [30] proposed an

automatic invariant feature based palmprint alignment method that deals with various image

distortions such as image rotation and shift. Two invariant features namely outer boundary

direction and end point of heart line have been introduced to align palmprints. Correct

identification rate can be improved up to 13% using this method. The local coordinate

system has been established using a few key points among fingers or in palm boundary. The

present alignment methods use it for detecting ROI and extracting it for matching and

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feature extraction. These alignment techniques give way a coarse alignment of the

palmprint images. These erroneous image alignments cause to a lot of missed and false

matches. For improving the accuracy of palmprint verification W. Li et al [31] presented a

palmprint alignment refinement method consists of application of iterative closest point

method to estimate the translation and rotation parameters after extracting the principal lines

from the palmprint image. Refinement in the alignment of palmprint feature maps has been

carried out with the help of the estimated parameters. Experimental results on the HK-

PolyU database showed improvement in the recognition accuracy and computational

complexity hence can be implemented in real time for verification as well as identification

of middle size database.

G. Amayeh et al [32] propose hand-based identification and verification using a component-

based approach which improves robustness and accuracy and also it avoids use of pegs.

This approach decomposes the hand silhouette in various regions. These regions correspond

to the back of the palm and fingers.The approach accounts for finger and hand motion. The

robustness and accuracy has been improved by fusing the information from various parts of

the hand.No guidance pegs or extraction of land mark points on hand is required when flat

lighting table is used for acquiring the hand images. Robust methodwhich uses iterative

morphological filtering system helps decomposing the silhouette of the hand in different

regions. By using an efficient method, high-order Zernike momentsbased region descriptors

helps in capturing the geometry of the back of the palm and the fingers. A database of 1010

images of101 subjects with 10 images and five enrollment templates per subject has

displayed a TAR = 99.98% when FAR = 0.1% and EER = 0.044 for verification and

99.98% accuracy for identification. System implementation on a 64-bits machine with 3.19

GHz processor and 2 GB of RAM, the Zernike moments computation for the fingers and the

palm using double precision architecture up to order 20/30 in Visual C++ Studio 2005 is

less than 0.01 s. The total preprocessing time required is less than 0.73 s for hand–arm

segmentation and finger–palm segmentation using MATLAB 7.4.0.Examinations with

option methodologies utilizing the entire hand or individual parts of the hand, outline the

prevalence of the proposed methodology both as far as accuracy and speed. Also, the

system performance found to be comparable or better when qualitatively compared with

schemes accounted in the literature. The approach using the whole hand or different parts of

the hand demonstrate the advantage of the proposed scheme. The approach has comparable

or better accuracy in terms of qualitative and quantitative comparisons with state-of-the-art

approaches. For developing non-invasive and hygienic biometric technology, G. Michael et

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al [33] propose an innovative touch-less palm print recognition system. Users are concerned

about touching the biometric scanners for many reasons. A low-resolution web camera is

used to capture the hand of user at a distance for recognition and hence users need not have

to touch any device for their palm print to be acquired. To track and capture the palm of

user in real-time video stream, a novel hand tracking and palm print region of interest

extraction techniques are used based on application of local binary pattern texture descriptor

on the palm print directional gradient responses. Improved performance has been achieved

when a modified probabilistic neural network is used for feature matching and verification

done in less than one second in the proposed system. Proposed system meets real-time

recognition challenges like hand movement, lighting change and variation in hand position

and orientation.

Effective biometric authentication based on palmprint uses minutiae features and features

like line features,delta features, geometry features,datum points. Line features are extracted

based on edge detection methods. J. Malik et al [34] pre-process the hand image to get the

preferred Region of interest. Sobel palmprint feature vector stores extracted features and

matching has been performed using Hamming distance method. The system accuracy has

been improved by Min Max threshold technique that matches a person with many threshold

values. Initially, reference threshold has been used for global authentication of person and

then for a person maximum and minimum thresholds are defined at local level. Min Max

threshold range is an effective method that increases accuracy of palmprint authentication

system. In this method the False Acceptance Rate is considerably reduced. The existing

schemes employ a fixed method for extracting features and similarity measurement but J.

You et al [35] extracted several features and implemented different matching measures at

different stages to attain high performance by a coarse-to-fine guided search. The approach

uses four-level features defined as global geometry-based key point distance (Level-1

feature), global texture energy (Level-2 feature), fuzzy interest line (Level-3 feature), and

local directional texture energy (Level-4 feature). The hierarchical multi-feature coding

method makes easy coarse-to-fine matching for proficient and efficient palmprint

authentication and recognition for a huge database. The technique has been tried with the

database of 7752 palmprint pictures from 386 unique palms. The utilization of initial three

level highlights can expel applicants from the database by 9.6%, 7.8%, and 60.6%,

separately.It has been viewed that mixture of four levels of features has a vast change

between diverse classes while keeping up a high minimization inside the class. The coarse-

level characterization by Level-1, Level-2, and Level-3 highlights is compelling and vital to

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decrease the quantity of tests fundamentally for further preparing at the fine level. The

Level-4 nearby surface prompts a quick look for the best match.L.Zhang et al [36] presented

a palmprint identification scheme that characterizes a palmprint using a set of statistical

signatures. The palmprint is initially transformed into wavelet domain and the directional

context of each wavelet sub band has been computed in order to collect the predominant

coefficients of its principal lines and wrinkles. A set of statistical signatures including

density, spatial dispersivity, gravity center and energy characterizes the palmprint with the

selected directional context values. These signatures have been used for classification and

identification. The experiments have been performed with two hundreds palmprint images

from fifty persons. The selected fifty individuals have been classified into eight categories.

The correct recognition rate observed to be 98%. The results are affected due to similarity

of signatures of few palmprints and observed as the limitation of the work. P. Shang et al

[37] proposed a palmprint recognition method based on multi-fractal spectrum using

statistical moment. The identification process initiates by capturing the palmprint image,

extracting and normalizing the sub-images followed by defining a coordinate system and

calculating partition function and estimating multi fractal spectrum. Three distinguishing

palmprint features have been used that include the width spread, maximum of multi-fractal

spectrum and a parameter that describes the asymmetry of the spectrum curve. Finally the

feature matching and palmprint identification has been carried out. Compared with the line

segments matching or interesting points matching based palmprint verification schemes, the

scheme uses a much smaller amount of data signatures. H.Li et al [38] proposed a scheme

for generating cancelable palmprint templates using a chaotic high speed stream cipher

based on coupled nonlinear dynamic filters that have flows inverse to each other followed

by generation of renewable and privacy preserving palmprint templates using the coupled

nonlinear dynamic filter chaotic stream cipher with multiple orientation palmprint features

obtained from a bank of Gabor filters and encoded in a phase coding scheme. Cancelable

templates have greater ability to discriminate palmprints from different hands by increasing

the inter-class deviation of dissimilar palms maintaining the intra-class distance among

palmprints of the same hands as compared with the standard palmprint templates. Finally

the matching is performed directly on the cancelable/encryption domain in parallel to

accelerate matching and to protect user‟s privacy. To obtain the final matching score

various fusion rules have been investigated for different directional palm codes. The Sum

rule accelerates the speed and improves the performance as compared with Max, Min,

Median and Product fusion rules. Experimental results on PolyU palmprint database verify

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the high performance and security levels with a very strong ability to reissue palmprint

templates. The approach has a high ability to reissue templates and achieves a better

separation of the genuine and the impostor populations with a zero equal error rate.J.You et

al [39] described a method to authenticate individuals based on palmprint identification and

verification using texture feature and interesting points. The proposed model is texture

based active selection system that make easy the fast hunt for best matching of the model in

the database in a hierarchical style. Texture energy measurements have a big discrepancy

among different classes whereas it retains high firmness inside the class. Dynamic selection

of a little set of like candidates from the database at coarse level directed by the global

texture energy distinguished with high convergence of inner palm resemblance. Also the

good quality dispersion of inter palm discrimination has been used for further processing.

For final confirmation an interesting point based image matching has been performed on the

selected similar patterns at fine level that has reduced the number of samples. Hausdorff

distance as the matching criterion can handle the partial occluded palmprint patterns. The

experimental results demonstrated the accuracy, robustness, efficiency, and effectiveness of

the algorithm.W.Kong et al [40] utilized 2D Gabor filter for extracting textural features for

palmprint authentication. Filter 11 found to be the best of 12 filters with regard to accuracy.

Matching process using hamming distance found to be translation and rotation invariant.

Experimental results demonstrate the effectiveness of this method.Orientation feature has

been demonstrated to be one of the most effective features for low resolution palmprint

recognition. W. Zuo et al [41] investigate the accurate orientation extraction and appropriate

distance measure problems for effective palmprint recognition using steerable filter.

Initially high order steerable filter have been used to extract accurate continuous orientation

and quantify it into discrete representation. For effective matching of accurate orientations,

a generalized orientation distance measure has been proposed. The distance measure for

matching of discrete orientations has been further extended and several existing distance

measures can be viewed as its special cases. The proposed method obtains state-of-the-art

verification accuracy on both Hong Kong PolyU and CASIA palmprint databases. The

proposed method enables small template size and satisfactory matching speed for practical

applications with support of a look up table. W. Jia et al [42] proposed robust line

orientation code for palmprint verification. Modified finite Radon transform has been used

for extracting the orientation feature of palmprint that helps in solving the problem of sub

sampling improvably. The problem of large rotations caused by imperfect preprocessing has

been solved by constructing enlarged training set. Matching algorithm that supports pixel to

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area comparison and haveimproved fault liberalcapability has been used. Investigational

results of verification on PolyU palmprint database demonstrate faster processing speed

andhigher recognition rate. Genuine acceptance rate found to be 98.37% while the false

acceptance rate is 4 × 10−5

% and its equal error rate 0.16%.

Coding based schemes are mainly promising palmprint recognition techniquesbecause they

have small size features, verification accuracy is high and matching speed is fast.F. Yue et

al [43] proposed a modified fuzzy C-means cluster algorithm to decide the orientation of

each Gabor filter based on the statistical orientation distribution and the orientation

separation. The method achieves higher verification accuracy as compared to the original

competitive coding scheme. Though improved competitive code with six Gabor filters

achieved higher verification accuracy than that with four filters; further increase in the

number of filters cannot significantly improve the verification accuracy. Considering

computational complexity and verification accuracy the competitive code with six

orientations would be the optimal choice for palmprint recognition. H. Imtiaz et al [44]

propose a feature extraction algorithm for palm-print recognition based on two dimensional

discrete wavelet transform that efficiently exploits the local spatial variations in a palm-

print image. A palm-print recognition system developed extracts histogram-based dominant

wavelet features from each of several spatial modules obtained after segmentation of the

palm-image into segmented modules. Feature dimension has been reduced by selection of

dominant features for the purpose of recognition; also capturing precisely the detail

variations within the palm-print image. The modularization of the palm-print image

enhances the discriminating capabilities of the proposed features and hence results in high

within-class compactness and between-class separability of the extracted features.

Daubechieswavelets (db1–db10) have been utilized for the purpose of feature extraction to

see the effect upon the recognition performance. Principal component analysis further

reduces the feature dimension. Extensive simulations on different standard palm-print

databases provide excellent recognition performance both in terms of recognition accuracy

and several ROC curves.Z. Guo et al [45] propose a algorithm for feature extraction, for a

local region,binary orientation co-occurrence vector represents the multiple orientations.

The binary orientation co-occurrence vector defines the local orientation features. It is

robust to image rotation. The equal error rate reduces from 0.0379% to 0.0189% by the

proposed binary orientation co-occurrence vector if the same Gabor filters are used as that

of in CompCode. Binary orientation co-occurrence vector can be stretched to other binary

feature extraction algorithms, likeRLOC, POC and orthogonal line ordinal feature. Binary

23

orientation co-occurrence vector outperforms the RLOC, CompCode and POC by reducing

the equal error rate significantly.

The identification accuracy of palmprint identification system is improved by storing

multiple templates for each subject. When a test palmprint image is applied to the system,

these templates are searched for a match to its nearest neighbor. For moderate or large scale

identification system to speed up the identification process F. Yue et al [46] proposed uses

of intrinsic characteristics of the templates of each subject to build a tree. This tree structure

then assists in fast nearest neighbor searching. The speed up in identification process is

further achieved by constructing a tree structure with virtual template and real templates of

each subject. The effectiveness of proposed strategies is demonstrated by adopting two

representative coding-based methods namely ordinal code andcompetitive code. The results

using the PolyU palmprint database (version 2) and a large scale palmprint database exhibit

faster nearest neighbors searching than brute force searching and with more templates per

subject in the database, the speedup becomes larger. This system is proves effective for

moderate scale embedded system and large scale PC based identification systems.

X. Pan et al [47] propose an improved 2D locality preserving projections forextracting

features straight from image matrices based on locality preserving criterion. The

improvements mostly concentrate on aspects. The nearest neighbor graph is constructed

taking each node corresponding to a column in the matrix, instead of the whole image,

forproperly modeling the intrinsic manifold structure. The two-dimensional principal

component analysis is applied in the row direction before2D locality preserving projections

in the column direction, for reducingthe final feature dimensions andthe calculation

complexity. Better recognition performance has been achieved in terms of both speed and

accuracy. The robustness of Gabor filter for variations advances the better-quality2D

locality preserving projections based on the Gabor features to further enhance the

recognition rate. The improved 2D locality preserving projections achieves 1.958%

improvement over two-dimensional locality preserving projections for the average

recognition rate when tested on a huge database DBII comprisingof 1730 images from 346

different palms, adopting a k-fold cross-validation for recognition. W. Zuo et al [48]

investigate the rationale of the post-processing approach using a Gaussian function and

demonstrate the mutual relationship between the post-processing approach and the image

Euclidean distance method. The post-processing approach has been extended to palmprint

recognition. The FERET face and PolyU palmprint databases have been used to evaluate the

post-processed linear discriminant analysis method. Recognition rate for linear discriminant

24

analysis based face and palmprint recognition has been improved using the post-processing

approach. Using three samples of each subject for training, the recognition rate of post-

processed enhanced Fisher model is 2.80% higher than that of enhanced Fisher model on

the FERET subset and 2.51% higher on the Hongkong PolyU palmprint database (version

2). J. Chen et al [49] propose a texture based approach for palmprint feature extraction

along with template representation and matching. The approach is effective, simple, flexible

and reliable because of symbolic aggregate approximation, a time series technology and 2D

data. On the PolyU database containing 7752 palmprints, this method achieves an equal

error rate of 0.3% for a one to one verification experiment and a rank one identification

accuracy of 99.90%. On CASIA database, an equal error rate of 0.9% can be achieved. The

approach has very low computational complexity to efficiently implement on slow mobile

embedded platforms. Local image features of palmprint, iris and finger-knuckle-print can be

appropriately extracted by using Riesz transforms in a unified outline. L. Zhang et al [50]

propose utilization of Riesz transforms for encoding the local patterns of biometric images.

The proposed schemes RCode1 and RCode2 uses Riesz transform. Both the methods uses 3

bits for representing each code and employs normalized Hamming distance for matching

purpose. RCode1 and RCode2 are evaluated. RCode2 attainsfairly similar authentication

accuracies with the state-of-the-art Comp Code technique and requires much less time at the

feature extraction step that condenses them improved candidates when time is considerable

factor.

S.Chakraborty et al [51] propose a method for palmprint based biometric authentication

utilizing the textural information existing on the palmprint by using the dual tree complex

wavelet transform. A histogram of the two dimensional image has been determined after

constructing the region of interest for the scanned color images of the palm. Thus enabling

utilization of a feature extraction module implemented using the one-dimensional dual tree

complex wavelet transform on the histogram signal. The dual tree complex wavelet

transform provides nearly shift invariant performance, reduced aliasing and directional

wavelets in higher dimensions. For authentication purposes utilizing extracted features back

propagation neural-network based binary classifiers are developed. Several real life scanned

color images of palms of individuals have been used for developing the system. The overall

mean accuracy of system is 98.35%. The utility of proposed system was further

demonstrated by employing it for CASIA database where also the system could achieve

mostly high average recognition accuracy of the order of 96-98%.

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L.Shang et al [52] propose a technique for palmprint recognition based on neural network.

This radial basis probabilistic neural network has been trained by orthogonal least squre

algorithm. Its structure has been optimized with the recursive least square algorithm. The

approach has been tested by exploiting a fast fixed point algorithm pre-processed PolyU

database for independent component analysis. The experimental results show that the radial

basis probabilistic neural network gets higher recognition rate and improved classification

efficiency than the-state-of-art classifiers. J.Kong et al [53] propose an approach for

handprint identification. Initially, region of interest is segmented through key points of hand

localization, and then the Gabor filtering and Zernike moment are used to extract the

palmprint features. The degree of similarity in the identification stage has been measured by

a two stage neural network structure. The proposed system makes the combination of SOM

and BP neural network for effective two stage personal identification. The system AR can

reach above 97.6%. The recognition performance of palmprint images degrade due to

variations occurring on the images. To handle the variations of rotation, translation and

illumination, raised by the capturing device and the palm structure, X.Pan et al [54] propose

an approach to extract local invariant features using Gabor function. The local invariant

features have been obtained by dividing a Gabor filtered image into two-layered partitions.

Further the differences of variance between each lower-layer sub-block and its resided

upper-layer block called local relative variance have been calculated. The global

disturbance occurred on palmprint images is counteracted as the extracted features only

reflect relations between local sub-blocks and its resided upper-layer block. The method has

been highly efficient for the local invariant features that are simple statistical quantities

without sophisticated calculation.Palmprint verification methods like palmprint orientation

code, robust line orientation code and competitive code are based on orientation coding with

fast matching speeds. Two distance measure types, SUM_XOR (angular distance) and

OR_XOR (Hamming distance) are used for orientation code. Z.Guo et al [55] propose a

unified distance measure having both SUM_XOR and OR_XOR as special cases and also

providedcertain principles for determination of the parameters for the unified distance.

Utilizing these methods of extracting features and coding techniques, the unified distance

measure has lower equal error rates than the original distance measures.

M. Mu et al [56] presentsoutline for extracting features for palmprint identification which

gives description of image that is shift a bleand invariant to gray scalethat to having high

identification accuracy at a low computational cost. The decomposition of the image has

been performed using the shift able complex directional filter bank which gives arbitrarily

26

directional resolution, two-dimensional decomposition of energy scalable and shift able

multi resolution, low redundant ratio andefficient implementation. The sub band

coefficients of complex directional filter bank decomposition have been operated by gray

scale invariant uniform local binary pattern and which covers information about the

distribution of the local micro-patterns. This results into local binary pattern mapping

having divided into many sub-blocks which are used for independent achievement of

statistical histogram. Fisher linear discriminant classifier has been learned in the statistical

histogram feature space for palmprint identification. PolyU palmprint database of 7752

images has been used for experiments. Several other multi-resolution and multi-directional

transform like dual-tree complex wavelet, Gabor filter and Contourlet transforms have also

been investigated. Complex directional filter bank produces the fine performance which

balances the identification accuracy with storage necessity and computational complexity

for extracting feature outline.W.Xuan et al [57] describe a texture based algorithm for

palmprint recognition combining 2D Gabor wavelets and pulse coupled neural network to

alleviate the limitation that the recent texture based algorithms of palmprint recognition

have unsatisfactory robustness to the variations of position, orientation and illumination in

capturing palmprint images. Initially the palmprint images have been decomposed by 2D

Gabor wavelets and then pulse coupled neural network is applied to imitate the creatural

vision perceptive process and decompose each Gabor sub-band into a series of binary

images. The features of these binary images are their entropies. Classification is performed

based on support vector machine based classifier. The proposed approach yields better

performance results in terms of correct classification percentages and high robustness to the

variations ofillumination, orientation and position in comparison with other texture based

approaches. X. Wang et al [58] describe on-line fast palmprint identification system. An

adaptive lifting wavelet scheme to decompose a palmprint image into several sub bands has

been constructed and then the pulse-coupled neural network used to decompose each sub

band into a series of binary images to reduce the computational cost of extracting palmprint

features from a palmprint image and make it easy to implement with hardware. Entropies

calculated from these images forms the features. Classification step involves support vector

machine-based classifier. The proposed approach yields a better performance in terms of the

correct classification percentages compared with the recent on-line palmprint recognition

algorithms. Low computational cost and ease of hardware implementation have also been

observed. L. Nanni et al [59] presents a method for personal authentication which uses palm

image. Three ensembles of matchers are designed that employ different feature

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representation schemes of the images like discrete cosine coefficients, invariant local binary

patterns and Gabor filters. To train the matchers, each ensemble is obtained by varying the

features. The three methods provide complementary information that has been exploited by

fusion rules. Palm based system is combined with other biometric characteristics that can be

extracted from the hand like middle finger, ring finger and hand geometry; thus obtaining a

further improvement in the performance. M. Fahmy [60] presents an application of Mel

frequency Cepstral coefficients for identification of palmprint. Initially for feature

extraction palmprint image has been transformed into one dimensional signal followed by

extraction of Mel frequency Cepstral coefficients from this signal. To help in the

recognition process, discrete wavelet transform of the one dimensional palmprint signals are

used. The Mel frequency Cepstral coefficients features of this discrete wavelet transform

have been added to Mel frequency Cepstral coefficients feature vector generated from the

original palmprint signal, to form a large feature vector that has been used for palmprint

identification. Feed forward back propagation error neural network has been used for

feature matching. The method is robust in the presence of noise. N.Alex et al [61] propose

use of multiple databases for high speed authentication using the acquired palmprint image

of the hand. Hand could be held in any pose providing hand independence for biometric

authentication. Appearance analysis has been used for feature extraction. It has been a

robust technique for detecting the extremities using a neighborhood scan method.H. Li et al

[62] outline cancelable palmprint binary orientation co-occurrence vector based on

anisotropic filter using feed forward feedback nonlinear dynamic filter and then fused the

independent matching scores at the score level to efficiently protect the security of template

and enhance the privacy of user. The system provides large re-issuance ability and fast

implementation for real robust applications. Experimental results on PolyU palmprint

database confirm EER to be 0.07%.A multi-scale method to represent curves sparsely is

Curvelet transform. Palmprint images are made up of several main curves. W. Xinchun et al

[63] utilize the curvelet transform to extract the feature information of palmprint images on

different scales, deal with the information by dimension reduction of principal component

analysis and then provide the information for RBF network for study and decision making.

The palmprint recognition has been performed as per requirements of customers by

choosing decision rules to get the fusion of the results. The method makes use of the

features information of images to obtain higher recognition rate than other methods.R.

Kumar et al [64] describe an automated approach to palm-print recognition. The system is

resistant to motion variance of the palm. A low pass Gaussian filter has been applied to

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remove the noise from the input image. Region of interest is extracted using the centre of

mass of palm-print image. Scale invariant feature transformation has been used further for

extracting and storing stable texture features from the region of interest. Biometric based

identification and security has been provided by comparing features extracted from the

region of interest with those from region of interest of other palmprint images.G.Badrinath

et al [65] proposed a technique that uses the instantaneous-phase difference got from

Stockwell transform of overlapping circular-strips for extracting palmprint features. The

Stockwell transform generates instantaneous-phase of user image in terms of time and

therefore it gives enhanced discriminant information regarding the user. It greatly increases

probability of identifying the user with. A low cost scanner has been used to obtain hand

images. Depending on their inherent characteristics, these hand images have been classified

as right or left hand then accordingly hand image is extracted from the palm-print. For

testing the system, IITK database consisting 549 images, CASIA database with 5239

images and PolyU databse with 7751 images were used. The performed in terms of

recognition rate found to be 100% correct. The equal error rate for all databases resulted

was less than 1% for all the databases.K.Wang et al [66] propose a Point-Line Duality

method for image registration in which a line in the image (x–y) space corresponds to a

point in the dual (h–q) space. Initially edge points have been detected and then linked and

segmented into chains, in a template image and a target image. Chains fitted to lines and

lines have been mapped to dual points in the dual space. A point merging algorithm deals

with the fragmentary line segments that should belong to a single line to improve stability

and efficiency. Line matching problem is converted to a point pattern matching problem.

Determination of registration parameters and matched line pairs is performed by a point

pattern matching algorithm. The technique has been effective for images under occlusion,

rigid body transformation and illumination change.X.Jing et al [67] presented an approach

that uses a two dimensional separability judgment to select the DCT frequency bands and

linear discrimination technique for face and palmprint recognition. The linear discriminative

features from the selected bands have been extracted by an improved Fisher face method

and the classification has been performed by the nearest neighbor classifier. The detailed

analysis showed the theoretical advantages of the approach over other frequency domain

transform techniques and state-of-the-art linear discrimination methods. For

experimentation purpose two face databases and a palmprint database have been used. The

approach significantly improves image recognition compared with the conventional

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discrimination methods. The approach reduces the dimension of feature space and

computing time.

Presently most of the palmprint recognition systems have active source as a key component

in the system to acquire images. To capture palmprint image, many data collection devices

have been used. The cameras with active light sources are popularly used to get good image

quality and high speed data capturing.Z.Guo et al [68] analyze the performance ofpalmprint

recognition under seven dissimilar illuminations together with the white light and based on

the experimental results on a huge database of multispectral palmprints conclude that

magenta or yellow light achieve higher accuracy in palmprint recognition as compared to

white light. D.Zhang et al [69] presented an online multispectral palmprint system.

Multispectral palmprint database has been established by capturing the palmprint images

under Red, Green,Blue, and near-infrared (NIR) illuminations by means of a data

acquisition device for investigating the performance recognition of each spectral band. It

has been observed that Red channel attains the best result while the comparable

performance by Blue and Green channels but are slightly inferior when compared with NIR

channel. To integrate the multispectral information palm line orientation code as extracted

features from different bands have been used with score level fusionsystem originated from

the set theory that reduces the overlapping effect between bands. The EER can be

significantly reduced as different bands highlights different texture information.

Considering higher verification accuracy and anti-spoofing capability the verification

results confirmedthat the multispectral fusion are superior to each single spectrum. The

fusion of Red and Blue hasachieved the best result. L.Zhang et al [70] initially analyzed the

fragile bits phenomenon and extend BOCV to E-BOCV by incorporating fragile bits

information in appropriate ways. They proposed to mask out the fragile bits when

computing the Hamming distance. Also they have proposed a metric FPD to measure the

dissimilarity of two fragile-bit masks. Extension of the original BOCV to E-BOCV has been

achieved by fusing the modified Hamming distance and the FPD. Experiments conducted

demonstrate that E-BOCV can achieve the highest verification accuracy.

W. Zuo et al [71] proposed a technique that deal with the issues of feature extraction and

classification simultaneously by using bidirectional principal component analysis

supplemented with an assembled matrix distance metric. Bidirectional principal component

analysis has been used for feature extraction by reducing the dimensionality in both column

and row directions. Assembled matrix distance metric has been used for classification to

calculate the distance between two feature matrices and then the nearest neighbor and

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nearest feature line classifiers have been used for image recognition. Bidirectional principal

component analysis achieved an average error rate of 3.55 using the ORL database with five

training images of each person for the neural network classifier and 2.70 for the neuro-fuzzy

classifier. Bidirectional principal component analysis achieved an error rate of 1.67 and

neuro-fuzzy classifier achieved an error rate of 1.33 on the PolyU palm print database.

P.Yeomans et al [72] proposed a palmprint classification algorithm that uses a multiple

correlation filters per class. The output of correlation filter produces a sharp peak while

filtering sample is of their class else it gives noisy output. Training of the filters is carried

out with the different regions of palmprint where high degree of line content is found on the

palm for every class. A simple line energy measure and line detection technique has been

used for scoring the palm regions. For training the filters of each class only the top ranked

regions have been used.While using the multiple correlation filter as classifier and training

the filter at same location (with respect to fiducial points), the algorithm performed better.

Using 64×64 pixels size multiple filters the classifier gained an average EER of 1.12 on

huge data base consisting 385 classes. The identification accuracy was found to be 99.88%

(five misclassifications out of 3465). D.Huang et al [73] proposed heuristic structure

optimization method for radial basis probabilistic neural network. Initially a minimum

volume covering hyper-spheres algorithm has been used for selecting the initial hidden

layer centers of the neural network followed by the recursive orthogonal least square

algorithm in combination with the particle swarm optimization algorithm to optimize the

initial neural network structure. The algorithm can solve practical problems having multiple

extrema and can simplify and accelerate the optimization process by efficiently initializing

the neural network. Also, the algorithm can optimize completely not only the hidden centers

but also the controlling parameter simultaneously. Eight benchmark classification problems

and two real world application problemshave been used for evaluating performance of the

algorithms. Experimental results show that the neural network achieves higher statistical

recognition rate, better classification efficiency and faster training and test speedscompared

toradial basis function neural network and multilayer perceptron in both the tasks.

Discriminant analysis is effective in extracting discriminative features and reducing

dimensionality. X. Jing et al [74] propose an optimal subset-division based discrimination

approach to enhance the classification performance of discriminant analysis technique.

Optimal subset-division initially divides the sample set into several subsets by using an

improved stability criterion and K-means algorithm. The optimal discriminant vectors from

each subset have been separately calculated. Projection transformation by combining the

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discriminant vectors derived from all subsets has been constructed. A nonlinear extension of

optimal subset-division based kernel discrimination approach has been provided. Kernel K-

means algorithm to divide the sample set in the kernel space and obtaining the nonlinear

projection transformation has been employed. These approaches are applied to face and

palmprint recognition and examined using the AR and FERET face databases and the

PolyU palmprint database. The approach outperforms several related linear and nonlinear

discriminant analysis methods. An approach which encodes the discriminant information

into the objective by protecting the projections called as discriminant locality preserving

projection, it also improves classification ability. Optimization of the objective function in

reproducing kernel Hilbert space enhance the nonlinear description ability of discriminant

locality preserving projection and called as kernel based discriminant locality preserving

projection. However, it suffers from the problems like: 1) larger computational burden; 2)

no explicit mapping functions that results in more computational burden when projecting a

new sample into the low dimensional subspace; and 3) it cannot obtain optimal discriminant

vectors that remarkably optimize the objective of discriminant locality preserving

projection. The weaknesses of kernel based discriminant locality preserving projection have

been overcome by X.Chen et al [75] with Hammerstein polynomial expansion. The method

directly implements the objective of discriminant locality preserving projection in high-

dimensional second-order Hammerstein polynomial space without matrix inverse that

extracts the optimal discriminant vectors without larger computational burden. Performance

of the technique has been evaluated through simulation on facial and palmprint feature

extraction and classification.J.Li et al [76] used two phase test samples representation

method for palmprint identification. Proper setting of the parameter (the number of the

nearest neighbors) is crucial for real world applications and found to be different for

different bands. For shorter band wavelength the number of the nearest neighbors becomes

smaller. Two phase test samples representation is a competent representation based

classification method and found to be computationally much more efficient than the original

sparse representation methods. For automatic classification of low resolution palmprints

X.Wua et al [77] proposed an algorithm that uses principal lines and number of

intersections. Initially principal lines in the palm image have been defined depending upon

their thickness and position. Set of directional line detectors have been used to extract the

potential beginnings of the principal lines followed by applying a recursive process for

extracting the principal lines in their totality. Six categories have been formed for

classifying the palmprints according to the number of intersections and the principal

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lines.The proportions of these six categories for the generated database containing 13,800

samples found to be 0.36%, 1.23%, 2.83%, 11.81%, 78.12% and 5.65% respectively. The

algorithm has shown classification accuracy of 96.03%. G.Chen et al [78] propose methods

for palmprint classification and handwritten numeral recognition by using the contourlet

features. The contourlet transform is a 2D extension of wavelet transform by applying

multi-scale and directional filter banks. The dominant features in palmprint images and

handwritten numeral images have been effectively captured using the algorithm. For

classification AdaBoost is used. The contourlet features are very stable features for

invariant palmprint classification and handwritten numeral recognition. Better classification

rates are reported when compared with other existing classification methods.

Because of the susceptibility of the biometric systems to database, replay and brute-force

attacks, these attacks must be analyzed before massive deployment of biometric techniques

in security systems. A.Kong et al [79] proposed a projected multinomial distribution for

studying the probability of successfully using brute-force attacks to break into a palmprint

system. Competitive Code has been used as the features and angular distance as the

matching scheme forms a projected multinomial distribution to describe the relationship

between the probability of the false acceptance and the number of attacks. Simulation has

been conducted to validate the model and the results demonstrate accurate estimate of the

probability for the model. Computationally it is not practicable to break this model into the

palmprint system with the use of brute-force attacks as the system threshold is set to lower

than 0.39. A.Jain et al [80] proposed and developed a prototype a system which is needed in

forensic applications which matches latent-to-full palmprint. Minutiae features have been

extracted from latent palmprints captured at 500 dpi or higher resolution. It is challenging

problem to match the Latent palmprint the latent prints which are lifted at crime sights

cover a small area of the palm, have poor quality and have a complex background and also

content less number of minutiae and creases as compared to full prints. The algorithm deals

with reliable estimation of the local ridge direction and frequency that makes possibleto

extractminutiae features and the ridges from poor quality palmprints. A fixed length minutia

code that captures texture and neighboring minutiae information has been utilized to

provide distinctive information around each minutia. In order to match two palmprints an

alignment based minutiae matching algorithm has been used. Performance of the algorithm

has been tested on two sets of partial palmprints against a database which consists of full

palmprints. For live scanned 150 partial and 100 latent palmprints, the system achieved

recognition rates of 78.7 and 69 percent respectivelyagainst a background database of

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10,200 full palmprints. Latent quality has a considerable effect on the matching accuracy.

The matching speed and matching accuracy can be significantly improved with additional

information like palm region, palm orientation and hand type. For the latents of same palm

a simple „OR‟ rank level fusion of these latents can be used for improving the matching

accuracy from 69 to 90 percent.

2.2Literature review of fingerprint as unimodal biometrics

The fingerprint foreground consists of oriented pattern with deviations in orientations.

Practically more robust segmentation methods are required due to existence of noise. For

segmenting the foreground in the fingerprint image firstly the global and local thresholding

methods have been used. B.Mehtre et al [81] used local histogram of ridge orientations for

secluding the area of fingerprint. For each block of 16 × 16 the histogram is computed and

at each pixel the ridge orientation is computed. An orientation pattern means foreground is

indicated as significant peak presence in the histogram means whereas background is

characterized by flat histogram. B.Mehtre et al [82] proposed a composed segmentation

method which uses the local histogram of the gray-scale variance of each block and

orientations. Low variance blocks are assigned to the background when no reliable

information acquired from the histograms. This algorithm is capable of handling the images

with uniform background i.e. background with white block. N.Ratha et al [83] assigned the

background or foreground to each 16 × 16 block with respect to the variation of gray-levels

in an orthogonal direction to the ridge orientation. The background has uniform variance

and direction independent. For orthogonal direction to the ridge the foreground has very

high variance and extremely low variance alongside the ridges.D.Maio et al [84] take apart

the region of interest. It is based on average magnitude of gradients in each block of image.

Presence of valleys and ridges makes the fingerprint area rich which results in high gradient

response in the fingerprint region and small for the background. L.Shen et al [85] proposed

a technique that convolves each image block with eight Gabor filters. They segmented the

fingerprint according to their quality using variance of the filter as “good,” “poor,”

“smudged,” or “dry.”A pixel-wise segmentation method has been proposed by A.Bazen et

al [86]. For each pixel they computed the mean of three features gradient intensity, intensity

variance, andcoherence.The pixel is associated with foreground or background by a linear

classifier. For each specific acquisition sensor the optimal parameters are learned by using a

supervised method.Elimination of the holes in foreground as well as background is the final

post-processing step morphological has been suggested by R.Gonzalez et al [87]. In a

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manual fingerprint matching process, experts were using a set of features, which were

established 105 years ago by F.Galton [88]. Fingerprint is represented by locally oriented

ridge and valleys; the discontinuities in the ridges are called as minutiae. Eighteen different

kinds of minutiae have been reported in [89]. In automated systems commonly used

minutiae features are ridge ending and ridge bifurcation.

The difficulty of automatic minutiae extraction has been carefully studied but

nevertotallyresolved. The performance of minutiae based verification/identification system

heavily depends on the fingerprint image quality. The fingerprint quality degrades during

the image acquisition method. Quality of fingerprint image and the various image

processing articrafts introduces the false minutiae to reduce the performance of the

algorithm. Several approaches to minutiae extraction have been reported, most of them

transform the gray scale fingerprint image to the binary ridge pattern. Basically the minutiae

extraction process primarily involves segmentation, ridge detection, thinning, and minutiae

detection. A critical task in minutiae extraction is ridge extraction. A global thresholding

method is not able to correctly segment the ridges from the background. B.Moayer et al [90]

proposed an iterative application of Laplacian operator and a pair of dynamic threshold for

bineration. At each iteration the fingerprint image is convolved through a Laplacian

operator and the pixels whose intensity is external with respect to range bounded by the two

thresholds are set to 0 and 1. The thresholds are progressively moved towards a unique

value so that a secure convergence is obtained. A similar approach has been proposed by

Q.Xiao et al in [91] where after convolution, a local threshold has employed. A fuzzy based

approach to image enhancement and the use of an adaptive threshold, form the basis of

bineration proposed in [92, 93]. The image is at first partitioned in little regions, which

processed separately. Each region follows smoothing and fuzzy coding of the pixel

intensities, contrast enhancement, bineration, counting of 1 and 0, fuzzy decoding, and

parameter adjusting. The sequence is repeated until the number of 1 approximately equals

the number of 0. Coetzee et al [94] proposed a new binarization technique based on the use

of the edges in conjunction with the grayscale image. Edge extraction is performed using

Marr-Hildreth algorithm [95]. L.O‟Gorman et al [96] presented a technique for image

enhancement and binarizationwhere the image convolution with some filters oriented

according to the directional image has been done. The filters are computed parametrically

with respect to the ridgeline characteristics. Since the filtering relies on directional

information, which extracts the ridge information from the poor images too. Short time

Fourier transform (STFT) is a recognizedmethod in signal processing for studying non-

35

stationary signals. S.Chikkerur et al [97] extend application of STFT to 2-D fingerprint

images for enhancement. The algorithm concurrently estimates the fingerprint intrinsic

properties like the local ridge orientation,foreground region mask andlocal ridge frequency.

Also they have proposed a probabilistic approach that robustly estimates these parameters.

The experimentations show that the technique performs favorably over other filtering

approaches in literature. M.Sepasian et al [98] explored the performance of fingerprint

based unimodal biometric system where three step procedures like 'Clip Limit', standard

deviation and sliding neighborhood is used for the identification and enhancement. The

fingerprints are enhanced using contrast limited adaptive histogram with the use of these

three steps. In the first step the contrast of the small tiles where the fingerprints existsis

enhanced using clip limit. To remove the artifact i.e. artificially introduced boundaries

because of clip limit process, the neighboring tiles are combined to the enhanced tile using

bilinear interpolation technique. In the second step, image is divided into non overlapping

blocks and they are discriminated by calculating the standard deviation of each block.

Which in turn, remove the background of image and calculate the boundary of region of

interest. In the last step, Minutiae (endpoints and bifurcations) in each specific pixel are

clarified using slide neighborhood processing. This process is also known as

thinningprocess.

Mehtre [99] proposed an algorithm using the directional image. The directional image,

which represents the local ridge direction in a 16 × 16 neighborhood, is first computed and a

set of eight 7 × 7 convolution masks is applied to the gray-level input fingerprint images to

improve the quality of ridge structure. Then, the ridges are extracted by applying a locally

adaptive thresholding method and a thinning operation is applied to the ridges. Finally, the

minutiae are obtained based on the computation of connection number. A post processing

stage based on the heuristics is used to remove the spurious minutiae. A method for

extracting structural features from fingerprint image has been proposed by N. Ratha et al

[100]. They viewed the fingerprint image as a textured image; orientation field has been

computed and used to design the adaptive filters in the minutiae extraction process. The

ridges are accurately located and detected by using a waveform projection segmentation

approach. The local maxima search detects the ridge pixels. The ridge skeleton image is

acquired and morphological operators are used for smoothing to detect the minutiae. The

post processing involves the removal of false minutiae from the detected minutiae. The

algorithm proposed by Ratha is robust to noise.

36

B. Sherlock et al [101] proposed a technique for fingerprint enhancement and binarization,

which performs a frequency domain filtering through position dependent filters. The filters,

which are constituted by directional band pass, enhance the image according to the local

ridge orientation and also remove the noise associated to both low and high frequencies.

D.Hung et al [102] enhances fingerprint image by equalizing the ridge width. The input

image is assumed to be a binary image. The directional enhancement of ridges is done after

estimating the local dominant direction. The enhancement process consists of direction

oriented ridge shrinking followed by direction oriented ridge expanding. The skeleton of the

enhanced image is obtained by Baja‟s algorithm. The algorithm also describes methods for

detecting bridges and breaks as separate features. A.Jain et al [103] described the design and

development of enhanced version of minutiae extraction algorithm projected by N.Ratha et

al [100]. They propose a hierarchical approach for obtaining a smooth orientation field

approximation of the input fingerprint image, to improve the performance of extraction of

minutiae. In the local neighborhood block the consistency level of the orientation field has

been computed and if it is to be higher than certain threshold then the local orientations

about the region are re-estimated at lower resolution level.Because of this a fairly smooth

orientation field obtained. The performance of a good algorithm for extracting minutiae

depends on the performance of the ridge segmentation method. Before the ridge

segmentation the directional filtering could be employed to enhance the fingerprint image at

the cost of heavy computations. Also the post-processing is an essential part of the minutiae

based verification systems.A fingerprint is the observable impression that papillary

produces when the papillary peak contact in a surface. The fingerprint impression is the

most established and the most well known feature utilized for identification or

authentication of individuals. The finger impression has novel highlights called minutiae,

which are focuses where a bend track completes the process of (ending), cross or branches

off (Bifurcation).L.Mary et al [104] dealt with the matter of selecting an optimal algorithm

for matching fingerprint based on minutia points for designing a system that matches

required condition in accuracy andperformance. B.Saropourian [105] have worked on the

single dots existing on the finger-print liked ridge-ending and bifurcation that works very

fine on the binary images and also gray scanned photo.. The observation is about finger-

print recognition that uses state of the single steak in the finger-print image, because

patterns of the veins in the finger-print without notice of the finger-print about person, and

can be used for identified person. J.Leon-Garcia et al [106] proposed an automatic

fingerprint recognition schemewhich usesinvariant moments andfingerprint features. Two

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algorithms namely Fast Fourier Transform and bank of Gabor filters are used for processing

enhancement image.An algorithm for extracting minutiae information is applied after the

enhancement process. This algorithm gives angle, coordinates and distance from the

minutiae. The invariant moments have been used for discriminationamong those

fingerprints that are confused. R.Kaur et al [107] notice the noise presence in fingerprint

images that directs to spurious minutiae. To surmount this problem, features are extracted

which efficiently decides the minutiae points in fingerprint. The technique can be used for

finding bifurcation and termination for matching the template. They also proposed an

algorithm smoothing for the features detection from fingerprints. The method finds the

ridges in the fingerprint image by using eight different masks. This process forms the binary

image of the ridges from grayscale fingerprint image. The experimental results illustrated

the algorithm accuracy in terms of false rejection rate false acceptance rate, and genuine

acceptance rate.C.Pornpanomchai et al [108] proposed the fingerprint recognition by

Euclidean distance method. The system uses a technique of image processing, which

consists of 3 major components, which are: 1) preprocessing component, the module that

reduces the noise of the original images and adjusts the sharpness of the lined pattern that is

the components of the fingerprint, 2) feature extraction component, the module that defines

the position of the core point used as a reference point and finds out the position of the

bifurcation points, and 3) fingerprint recognition component, the module to compare the

shape context of training and testing data sets. Based on the experimental results, the system

has acceptable accuracy with average access time of around 19.68 seconds per image.

K.Nandakumar et al [109] presented a fully automatic person identification system based on

fingerprint minutiae. They remove high curvature points got from the fingerprint impression

introduction field and utilization of them as aide information to adjust the format and

queryminutiae. The assistant information itself doesn‟t release any data about the details

layout, yet contain adequate data to adjust the format and question fingerprints

precisely.Further, application of a minutiae matcher during decoding leads to significant

improvement in the genuine accept rate. They have shown that improvedperformance can

be attained by the use of multiple fingerprint impressions throughverification and

enrollment.

Most of the biometric systems which uses fingerprint store the minutiae templates of an

individual in database. Traditionally it is assumed that the original fingerprint information

of user is not disclosed by minutiae templates. A.Ross et al [110] illustrated that three

information levels concerning the parent fingerprint can be extracted from the minutiae

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template only, viz., 1) the information of orientation field, 2) the type orclass information,

and 3) the structure of friction ridge. Using the evidence of minutiae triplets, the orientation

estimation algorithm find out the direction of local ridges. The class of the fingerprint can

be predicted with the given minutiae distribution and the determined estimation orientation

field.The streamline based on the approximated orientation field generates the ridge

arrangement of parent fingerprint.The texture is imparted to resulting ridges by using line

integral convolution.It results in ridge map which looks like parent finger. Ability of

preserving the minutiae in restructured ridge map at specified locations is the salient feature

of this non iterative technique. J.Ravi et al [111] projected a fingerprint recognition

technique that uses minutia score matching.Block filter have been used for thinning the

fingerprint image, at the boundary it scans the image to preserve the image quality and the

minutiae extractedfrom the thinned image. Experimentations showed that the false matching

ratio is better compared to the existing algorithm. H.Wang et al [112] established theorems

for designing the robustness templates of fingerprint feature extraction cellular

neural/nonlinear networks, which are able to extract the endings and bifurcations as two

important features in a fingerprint image. The theorems provide the template parameter

dissimilarities to determine parameter periods for employing the corresponding functions.

Simulation result shows the effectiveness of the methodology. For building minutia matcher

and a minutiaextractor M. Kaur et al [113] combined many methods. The grouping of

multiple techniques comes from a extensive study of research papers. in additiona few novel

changes similar to segmentation by Morphological operations, false minutiae elimination

methods, enhanced thinning, special consideration of triple branch counting forminutia

marking, minutia unification using decomposition of a branch in three terminations, unified

x-y coordinate system matching subsequent to a two-step transformation use.

C. Srinivasa et al [114] used virtual minutiae for fingerprint recognition, here the virtual

minutiae is used for reconstruction of fingerprint orientation as well as to enhance the

system performance during the matching. The conventional minutiae matching arecombined

to reconstructed orientation field by decision fusion process. As orientation field is

asignificant global feature of fingerprints, the proposed techniquecan achieve better results

than that of conventional techniques. Fingerprint ridge particulars are usuallyexplained in a

hierarchical order by three different levels i.e. Level 1 (pattern), Level 2 (minutia points),

and Level 3 (pores and ridge contours). Though latent print auditors often take benefit of

Level 3 features to help in recognition, Automated Fingerprint Identification Systems

(AFIS) at present rely only on Level 1 and Level 2 features. In fact, the Federal Bureau of

39

Investigation‟s (FBI) standard of fingerprint resolution for AFIS is 500 pixels per inch (ppi),

which is insufficient for capturing Level 3 features, like pores. With the progress in

fingerprint sensing knowledge, many sensors are at the presentready with dual resolution

(500 ppi/1,000 ppi) scanning ability. But, with the increase in the scan resolution only does

not essentiallyoffer any performance improvement in fingerprint matching, exceptutilization

of an extended feature set. It results in need of a organized study to find out how much

performance gain can be achieved by the introduction of Level 3 features in AFIS is

extremelypreferred Anil Jain et al [115] proposed a hierarchical matching scheme which

uses extracted features from all the three levels from 1,000 ppi fingerprint scans. Gabor

filter and wavelet transform are used for automatic extraction of Level 3 features like ridge

counters and pores and Iterative Closet Point algorithm is used for matching.The

experimentation proves that Level 3 features hold important discriminatory information.The

matching system equal error rate (EER) relatively reduced by 20% if Level 3 features are---

-.There is a relative reduction of 20 percent in the (EER) of the matching system when

Level 3 features are utilized in grouping with Level 1 and 2 features. This noteworthy

performance gain is constantly observed across a variety of quality fingerprint imagesPartial

fingerprint recognition has a crucial step of alignment of high resolution partial

fingerprints.The inadequate feature resulted from fragmentation of small fingers makes the

methods unsuitable for fingerprint recognition. It is observed for previously developed

fingerprint alignment techniques which includes both the minutiae based and non minutiae

based also.

Q. Zhao et al [116] proposed a pore based new approach for alignment of high resolution

partial fingerprints. Pores are a category of fingerprint fine ridge features which are present

in a plenty of numberseven on minute fingerprint areas. A difference of Gaussian filtering

approach is used for extraction of pores.After extraction of the pores, characterization of the

pores is done using pore-valley descriptor which is based on their orientations and locations,

as well as valley structures around them and orientation fields.For locating pore

correspondence a coarse-to-fine core matching algorithm is developed which is based on

pore-valley descriptor.The alignment transformation between two partial fingerprints can be

estimated because of determination of corresponding pores. Two fingerprint matchers and

high resolution partial fingerprint dataset are established for comparing the method with

orientation field based and minutiae base methods.The PVD based method experiments

resulted in locating the corresponding feature points accurately and estimating the

alignment transformation properly.Hence for the high resolution partial fingerprint

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recognition the accuracy significantly improved. M.Islam et al [117] made a comparative

study by implementing number of methods on graph based matching,global features

matching and evaluated the performances by false rejection rate and equal error rate and

found that the authentication matching performance varies on the use of different

enhancement methods along with the images quality gained by means of various

sensortypes. A. Aburas et al [118] motivated to present another way to tackle the problem

that is relies on the properties of Huffman coding algorithm. No additional verifications are

needed. All you need is the image itself and go ahead. The obtained results are very

promising in terms of simplicity, reliability, and cost (time and storage).The output of filter

or a transform can also be used for representing the fingerprint image.The filter output in

the form of some statistical measure like variance has been used for matching (as

feature).Gabor transform possesses the frequency localization and orientation, found to be

useful for extracting features from fingerprint.Using the orientation and frequency

selectiveness of Gabor transform, the authors in [119-122]used the Gabor transform and

reported the results.The strong directionality in fingerprint pattern is represented as

orientation field. The first orientation image was introduced by Graselli[123].

The local orientation of the fingerprint ridges is encoded as the element of matrix,this

matrix is called as orientation image. Each element of this the grey level watershed methods

can be used to locate the ridges on a particular fingerprint image. Using this an efficient

fingerprint technique is described by G.Rao et al[124]. The inked images or scanned

fingerprints are as metrics for describing identification technique performance.These

metrics are given fed to gray levelwater shed method. Five level decomposition is

performed to achieve the accuracy in matching the images. The better performance in

comparing the resulting fingerprintscan be achieved by using these parameters. A database

consisting 7 images when tested on this system is found to be faster and accurate for the

fingerprint matchingprocess study.A fingerprint verification method was proposed by

Helfroush et al [125]. This method is based on introduction of feature extraction from

spectrum and direction domains of fingerprints.Here the fingerprint spectrum is divided into

sector. Each sector contains equal number of pixels, the respective statistical parameters

standarddeviation and average is calculated.For any fingerprint this feature vector can be

used for calculating distance measure of different fingerprints. A particular value can be

assigned to the resemblance of Block Directional Filed (BDF) of the two fingerprints.To

verify the input fingerprint image, the fusion of BDF and the above mentioned distance is

used.The simplicity and no requirement of preprocessing makes the verification speed high.

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Moreover unlike the other image based methods, reference point detection is not needed.

The experimentation results for verification accuracy are better when compared with the

other important methods in the literature.

B. Kumar et al [126] adopted an algorithm that uses Short Time Fourier Transform

(STFT)for fingerprint image enhancement. For analyzing non-stationary signals, STFT is a

well known time-frequency analysis technique. The existing fingerprint feature extracting

algorithms based on predominantly local landmarks (e.g., minutiae-based) or exclusively

global information. The fingerprint texture that relies on extracting one (or more) invariant

points of reference of the fingerprint texture based on an analysis of its orientation fieldhave

been used. A predetermined region of interest around the reference point is circular

tessellated into cells. Each cell is then examined for the information in one or more

different, orientation specific, spatial frequency channels. An ordered details of the features

thus extracted from each cell is used as the representation of the fingerprint. Thus, the

representation elements capture the local information and ordered enumeration of the

tessellation captures the invariant global relationship among the local patterns. The local

discriminatory information in the sector, decomposed by Gabor filter bank and compute the

average absolute deviation from the mean of gray value intensities is called the Finger

Code. Fingerprint coordinate is the way to the scheme and consequences for the efficiency

and precision of the entire system straightforwardly. Fingerprints are matched essentially in

light of their texture pattern, which can be described with the orientation field of

fingerprints. A fingerprint, having different orientation angle structure in different local area

of the fingerprint and if this texture pattern correlates amongst the adjacent local areas of

the fingerprint, can be observed as a Markov stochastic field. H.Guo [127] and D.Singh et al

[128] presented a fingerprint matching technique, based on embedded Hidden Markov

Model that is used for representation the orientation field of fingerprint. Matching

Embedded Hidden Markov Model parameters gives robust and accurate fingerprint match.

After the process of feature extraction from fingerprint these parameters are built, forming

the samples of observation vectors and training the embedded Hidden Markov Model

parameters. N.Ratha et al [129] proposed an accurate,efficient and distortion-tolerant

technique for fingerprint authentication which uses graph representation. For the reference

and query fingerprint the weighted and labeled minutiae graphs has been constructed. In the

first phase minimum set of matched node pairs have been obtained by matching their

neighborhood structures. The second phase included more pairs in the match by comparing

distances with respect to matched pairs obtained in first phase. An optional third phase,

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extended the neighborhood around each feature, was entered if it cannot arrive at a decision

based on the analysis in first two phases. Simple and intuitive cost functions form basis of

the algorithm and the robustness of the algorithm has been substantiated by experimental

results on large databases. Various parallel implementations to speed up the algorithm have

been considered .S.Parthasaradhi et al [130] described a fingerprint scanner susceptible to

spoofing using artificial materials. For fingerprint scanners, a liveness detection using an

anti-spoofing method has been developed. The technique quantified a pattern in fingerprints

(specific temporal perspiration), acquired from live claimants. Enhanced perspiration

detection algorithm performs better than state-of-the-art work on fingerprint scanner

technologies. The results show an improvement in past reports by decreasing the time

needed to make the decision and demonstrating its applicability to a variety of fingerprint

sensor technologies. Diverse subject populations provide approximately 90% classification

rate for all scanners. The method is totally software based and no additional hardware is

required.

The recognition of a person needs a comparison of his fingerprint with the fingerprints

which are stored in a database. As in civilian and forensic applications this database may be

extremely large. For these situations the identification process takes more response time. It

can be speed up by minimizing the comparisons required to be performed.This can achieve

by dividing the fingerprint database into number of bins. These bins can be formed on

predefined classes. Hence the fingerprint to be recognized is then required to compare with

the fingerprints from one bin in database only which depends on its class.Thus, fingerprint

classification refers to the problem of assigning a fingerprint to a class in a consistent and

reliable manner. The geometric features like delta and core point of fingerprints are used for

classifying fingerprints. F.Galton [88] was the first who studied fingerprint in depth.Two

more classes are added by E Henry [131] to the classes proposed by Galton.Galton - Henry

classification techniques consists of tented arch ,right loop, left loop, classes arch, and

whorl.

For automatic fingerprint classification several methods are used such as statistical,

syntactic, rule based, structural, neural network and classifier approach. Here some of them

are presented. Using poin-care indexthe type and position of singular points is found. It is

used for a coarse classification [132]. The ridge line flow is traced to get finer classification.

To improve the classification accuracy,valid numbers of singularity points are detected by

iterative regularization[133]. Hong et al introduced more robust technique [134].They

togetherly used number of ridges found in the image and number of singularities are used in

43

a rule base approach.This distinct feature combination leads tobetter performance.In a

classification method,B Cho et al[135] used the loop point only. They used the orientation

of the fingerprint area near the loop and the curvature for fingerprint classification. A.Jain et

al [136] proposed ridge geometrical shape based classification. They defined fingerprint

kernel for each class. By finding the best fitting kernel for orientation filed of particular

fingerprint, the classification is done. The classification using structural approach is

proposed by D. Maio et al [137]. The partition of orientation image is done using

minimizing cost function. The minimizing cost function considers the element orientation

variance in each region.Then the classprototype is compared with relational graph using

inexact graph matching method.Template based matching for guiding the partitioning of the

orientation image proposed by R.Chappelli et al [138]. Since this approach only relies on

global structure information, it can deal withpartial fingerprints which contain no singularity

points sometimes. It also works on noisy images.Classification of fingerprint using hidden

markov model classifier is proposed by Senior [139]. A.Jain et al [140] proposed a k-nearest

neighbor statistical classifier. KL transform has been used for fingerprint classification by

R. Chappelli et al [141, 142].B.Vikram et al [143] proposed a technique of using the image

parameters like median, mean, standard deviation, root mean square and variance value to

train fuzzy inference system based adaptive network for classifying the test image. The

image classification is done as arch, tented arch, right loop, left loop, twin loop and whirl.

Using the matching algorithm this classification permits us to proceed with the images

which fall in theexactinggroup of ridge structure thus saving time.

D.Batra et al [144] used a Gabor filter-based Feature extraction scheme to generate a 384

dimensional feature vector for each fingerprint image. The classification of these patterns is

done through a two stage classifier in which K Nearest Neighbor (KNN) acts as the first

step and finds out the two most frequently represented classes amongst the K nearest

patterns, followed by the pertinent SVM classifier choosing the most apt class of the two.

Finally six SVMs have to be trained for a four class problem, that is, all one-against-one

SVMs. The maximum accuracy reported as 98.81% with a rejection percentage of 1.95%.

The SVM training time was 145 seconds, i.e. 24 seconds per SVM on a Pentium III

machine. B.Gour et al [145] proposed a system to group the fingerprints based on the ART1

neural network. The clustering quality of ART1 supported clustering method has been

compared with the Self Organizing Neural Network clustering algorithm. They have also

presented a multilevel classification method for a fingerprint generating class-code for

eachminutiaon in a fingerprint.

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U.Rajanna et al [146] presented fingerprint classification by comparing four different

feature extraction techniques i.e. orientation maps (OMs), Gabor filters, minutiae maps and

orientation co linearity based feature extraction. They also studied the issue of rank-level

fusion method to improve classification performance. They concluded that the performance

of OMs is more in comparison to Gabor features because features extracted by Gabor filter

are sensitive to errors in localizing the registration point. Similarly, combinations involving

OMs improves during fusion of ranking improved its performance demonstrate the

importance of orientation information for classification purpose. Thus OMs achieved best

classification with orientation collinearity classifiers. V.Conti et al [147] presented a

technique in which pseudo-singularity-points were detected and extracted to make possible

fingerprint classification and matching.

For current fingerprint indexing schemes, global textures and minutiae structures are usually

utilized. To extend the existing methods of feature extraction, S.He et al [148] studied the

three most popular local descriptors for fingerprint indexing and give a comparison of

indexing performance for evaluation of these three features on public fingerprint databases.

For index construction, the locality sensitive hashing is used to efficiently retrieve similarity

queries in a small fraction of the database. Experiments show that all the features perform

equally well.Latent fingerprint recognition is of serious significance to law enforcement

groups in forensic applications. Though incredible development has been made in the field

of automatic fingerprint matching, latent fingerprint matching persists to be a hard problem

since the challenge concerned are fairly dissimilar to plain or rolled fingerprint matching.

The main complexities like small finger area, large non-linear distortion and poor value of

friction ridge impressions are a few of them latent fingerprint matching A.Jain et al [149]

proposed a scheme that matches latent images with rolled fingerprints considering the

precise characteristics of latent matching difficulty. With minutiae, extra features similar to

quality map and orientation field are also utilized in the scheme. Experimental outcomes

point that introducing the quality map and orientation field to minutiae-based matching

directs to good quality detection performance in spite of the inherently complicated nature

of the problem.

K. Saini et al [150] have made a fingerprint matching system to provide a security to a

virtual process. The fingerprint detection method may undergo attacks at different points

through the verification procedure. The mainly general attacks arise by using the fake

fingerprint at some stage in capturing the image. The broadcast channel connecting the

feature extractor and matching might also be interrupted and the fingerprint characteristic

45

may be stored for later use. The major difficulty relies on differentiating a live finger from

the synthetic material made finger. The types of attacks, fingerprint damage or stolen by

some another person, effect of illness are some of the topics discussed here. This approach

eliminates these kinds of attacks by means of a virtual system. Along with the virtual

system this paper also considers aspects related with security of fingerprint. Some aspects

related to secure fingerprint is also be considered in this paper.

Security of biometric information is gaining attention and digital watermarking methods are

used to protect the biometric information from either intentional or accidental

attacks.Amongst the different biometrics, fingerprints are more popular for authentication,

as they are unique for individual and are primarily used for the instant establishment of

instant personal identity. However, they are vulnerable to intentional or accidental attacks,

when transmitted over network. Thus, a protective scheme is required which will protect

fidelity and avoid alterations. D.Mathivadhani et al [151] considers two techniques that

protects of fingerprint biometric data using digital watermarking techniques. Both the

techniques discussed are based on Discrete Wavelet Transformation (DWT). From the

experimental results, it can be concluded that both techniques provide adequate security to

the fingerprint data without degrading visual quality. Further, the verification performance

after dewatermarking is also analyzed.

2.3Literature review ofmultimodal biometrics

Generally biometric system which uses single modality for the recognition purpose is called

as unimodal biometric system. The modalities in unimodal biometric system such as

fingerprint, face, palmprint, FKP, iris and voice are somewhatsusceptible to the problems

such as intra class variation, noisy data , non versatility, non-universality, spoof attacks, etc.

which results in high FRR and FAR. Therefore to conquer these limitations andprovide

higher security features a typical solution is to use the multimodal biometrics instead of

unimodal. Here more than one modalityused jointly to validate the individuality of a person.

But the high error rate in the presently available multimodal biometric systemsfound in

state-of-the-art literature does not found suitable for online recognition systems [152].

Multimodal biometric system involves utilization two or more modalities for the personnel

identification purpose like finger and iris [153], palm and finger [154], face and palm [155],

face and finger [156] etc..Previously many literatures have proposed Multimodal biometric

[157, 158]. It is found that except hand geometry (palmprint) and fingerprint, remaining all

modality has weakness in their characteristic. The fusion of these modalities is the vital

46

requirement for multimodal biometric systems.Sensor level, feature level, match level and

decision level are the different levels where the fusion of these modalities can be achieved.

L. Hong et al [159] present an approach to integrate face and fingerprint for personal

identification. Face recognition demonstrates to be fast but unreliable whereas fingerprint

detection is consistent but incompetent in database recovery. A model biometric method

that incorporates fingerprint, face and surmount the limitations of face identification

systems as well as fingerprint verification systems. The integrated prototype system

operated in the identification mode with an admissible response time. The system

establishes the identity which is reliable more than any face recognition. The performance

improvement at decision fusion is achieved by integration of multiple cues and different

confidence measures.The decision fusion scheme assumed that the similarity values

between faces are statistically independent of the similarity values between fingerprints.

Experimental results demonstrated the system performance with good response time and

accuracy.Y.Isobe et al [160] present a biometrics-based personal authentication system

using combination of a smart card, a Public Key Infrastructure (PKI) such as X.509

certificate and fingerprint verification technologies. The legal aspects of using the personal

authentication system described in the report have been considered for application to

construct a public key platform such as the digital signature method by means of X.509

platform.

S. Ribaric et al [161] develop multimodal biometric recognition system that uses the human

hand features using eigen palm features and eigen finger .In this method, the fusion was

used at matching-score level. The identification method has been separated into image

capturing, extracting and normalizing the palm, preprocessing, and strip-like finger sub

images; extracting the eigen finger and eigen palm features base on the K-L transform;

matching and also fusion; and a (k, l)-NN classifier and thresholding based decision.. The

method has been tested with database of 1820 hand images of 237 people. The results prove

the efficiency of the method in terms of the recognition rate (100 percent), the equal error

rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent). The unimodal

biometric systems based on single biometric information are all the time affected by

problems likesusceptibility to circumvention, no-universality andnoisy sensor data. F.Yang

et al [162] combined palm-print, finger print, and hand-geometry for person

individualityauthentication. Dissimilarto the other multimodal biometric methods, the user

does not need to experience the problem of using two separate sensors, as all the three

biometrics taken from the same image. Wavelet transform has been utilized for extracting

47

the features from palm-printand fingerprint. The hand-geometry features (like width, length)

arealso extracted following the pre-processing stage. Mach score fusion and Feature fusion

are jointly employed to set upindividuality. The system has been tested on a database of 98

persons. Multimodal biometric system has some disadvantages like expensive devices,

required more resources for computation and storage causing some inconvenience to the

user. G.Amayeh et al [163],illustrated a verification system based on hand which uses

segmentation and fusion of fingerand palmprint. The 2D images obtained by placing the

hand on a planar lighting table with no use of guidance pegs are used for this system.

Without the extracting any landmark point on the hand, the finger and palmprints are

segmented. At first a morphological operators based robust iterative technique is used for

segmenting the hand from forearm. Again the morphological operators are used for

segmenting the hand into six regions. These six regions correspond to the fingers and the

palm. High order Zernike moments calculated by using an efficient method represents the

geometry of each component of the hand. Verification is carried out by the fusion of the

information from various parts of the hand. A database of 101 subjects has been used for

evaluating the system. The system illustrated robustness and high accuracy. Comparing

with competitive approaches that uses whole hand demonstrated the dominance of the

component-based approach with reference to accuracy and robustness.A.Meraoumia et al

[164] integrate FP and FKP in order to construct an efficient multi-biometric recognition

system based on matching score level and image level fusion. To determine the

effectiveness of the method, the work uses minimum average correlation energy (MACE)

and Unconstrained MACE (UMACE) filters in conjunction with two correlation plane

performance measures, max peak value and peak-to-sidelobe ratio. The experimental results

show that the designed system achieved an excellent recognition rate on the Hong Kong

polytechnic University (PolyU) FKP and high resolution fingerprint database.

R. Telgad et al [165] develop multimodal biometric system by using face and fingerprint

multimodalities. The system takes the advantage of individual Biometric System. The

fusion of face and fingerprint modalities at score level fusion has been presented. The

system extracts the features used for matching. Euclidean distance matcher has been used

for face and finger print modalities. Fingerprint recognition has been done with the help of

minutiae matching and Gabor filter. Face feature has been extracted with the help of PCA

(Principle Component Analysis) for dimensionality reduction. The match scores are then

normalized and sum score level fusion used to develop the system. The recognition rate has

been increased and the error rate decreased with the help of this systemM.Ferrer et al [166]

48

present a multimodal biometric recognition method which combinesthe geometrical, finger

and palm print features of human hand. The right hand images captured using commercial

scanner having 150dpi resolutionsis used.Using Support Vector Machines as verifier,

geometrical feature are gained from binarized images having 15 measures, the geometrical

features are computed. Various 2D Gabor phase encoding methods are used to obtain the

palmprint and fingerprint textures. For assuring the image alignment, a robust coordinate

system is defined. For verifying the individuality the threshold and a Hamming distance is

used. The improvement proved by a score, decision and feature level fusion. A.Kumar et al

[167] present an approach for authenticating persons by the use of triangulation of hand

vein images and knuckle shape information extraction at the same time. The scheme is fully

automated and uses palm dorsal hand vein images obtained by contactless imaging which is

near infrared and the low-cost also. Considering the knuckle tips as key point, extracting the

region of interest and image normalization have been done. The matching scores have been

generated in two parallel stages. The first one is the hierarchical matching score from the

four topologies of triangulation in the binarized vein structured and the geometrical features

consisting of knuckle point perimeter distances in the acquired images. The weighted score

level grouping from these two matching scores have been used to validate the persons. The

results from the method using contactless palm dorsal-hand vein images are shows potential

with equal error rate of 1.14% and suggested more user friendly option for user recognition.

From the same image simultaneously the unique knuckle point perimeter distances are

extracted which are utilized for achieving the performance enhancement. The usefulness of

knuckle shape has been quoted in biometric literature with negligible attention for usage.

For the database of 100 users the experiments result illustrated 1.14 percent EER. The

demonstrated performance are to be taken in the context of touch less imaging as presence

of higher intra class variations is expected in these images as compared to the images

obtained from fixed imaging devices with hand docking.

L. Nanni et al [168] presented a trained technique to combine biometric matchers at the

score level. The technique is based on a combining machine learning classifiers trained by

the use of the match scores from various biometric advances as features. Finite Gaussian

mixture model parameters have been used to model the genuine and imposter score

densities through fusion method. Numerous tests on different biometric authentication

methods that relates to fingers, fingerprints, hand geometry, palms, and faces prove that the

technique outperforms other trained and non-trained approaches to combine biometric

matchers.It has likewise been tried on distinctive classifiers like support vector machines,

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AdaBoost of neural networks, also on their random subspace versions, representing that the

technique has Random Subspace of AdaBoost. The densities expected using a mixture of

Gaussian models has been used for training a machine learning classifier. The likelihood

ratio based feature coupled with a Random Subspace of AdaBoost of neural networks

attains a low Equal Error Rate in numerous tests with no parameter alteration for each

dataset. A.Tharwat et al [169] propose two multimodal biometric authentication methods

using ear and FKP images based on image-level fusion along with a multi-level fusion

method at image and classification levels. The fusion of images of ear and FKP has been

performed before feature level and hence there is no information loss. The features have

been extracted from the fused images using different classifiers and then the outputs of

these classifiers in the abstract, rank, and score levels of fusion have been combined.

Experimental results show that the authentication method increase the recognition rate

compared to the state-of-the-art methods. Experiments show that score level report better

results than rank or abstract levels. The processing time of extracting the features from ear

image has been small compared with FKP image because the dimensions of ear image are

smaller than FKP image.Y.Chen et al [170] propose a scheme that uses linear regression

and expand a method called reconstructive discriminate analysis (RDA) for extracting

feature and reducing the dimensionality. RDA has been provoked from linear Regression

Classification (LRC). LRC suppose that each class lies on a linear subspace and locates the

nearest subspace for a particular sample. RDA has been intended for matching the samples

with their nearest subspaces that describes the intra-class rebuilding scatter also the inter-

class rebuilding scatter, looking for finding the projections that concurrently maximize the

inter-class reconstruction scatter and minimize the intra-class reconstruction scatter. RDA

can also be considered as another type of classical linear discriminant analysis (LDA) from

the reconstructive observation. The technique has been applied to FKP and face

identification on the ORL, extended YALE-B, FERET face databases and the PolyU FKP

database. The projections of RDA expose and divide the subspaces matching to different

classes in the condensed subspace. The investigational results on three face image databases

and one FKP database showed that RDA plus LRC has been extra efficient than other

grouping of dimensionality reduction methods and classification. Comparing with LDA,

RDA has natural connections to classifiers as RDA has been induced from LRC. LRC can

completely use the feature of the RDA subspace. RDA and LRC can be flawlessly included

into a pattern recognition system. RDA can extract more features than LDA. Just like LDA,

RDA also suffered from the small sample size problems. A.Meraoumia et al [171] propose

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an efficient online personal identification system based on FKP using the Gaussian Mixture

Model (GMM) and two-dimensional Block Based Discrete Cosine Transform (2DBDCT).

A segmented FKP has been divided into non-overlapping and equal-sized blocks and then

the 2D-BDCT over each block has been applied. By using zigzag scanned order each

transform block has been reordered to produce the feature vector. The GMM for modeling

the feature vector of each FKP has been subsequently used. Log-likelihood scores have

been used for FKP matching. Experimental results show that the proposed method yield

best performance for identifying FKPs and able to provide excellent identification rate and

provide more security. M.Natarajan et al [172] proposed a security system that deals with

Multi-Modal biometric features namely FKP and fingerprint for recognition and

verification. For every transaction, a distinct random key has been generated from the user‟s

samples. Random Triangle Hashing method has been used for random key generation.

Shuffling process has been used for key management and reliable transaction. The system

keeps secret the content of information from unauthorized parties, detecting the alteration of

data, identifying data origin and preventing an entity from denying previous actions.

L. Lu et al [173] proposed a finger multi-biometric cryptosystem using feature-level fusion

to simultaneously protect multiple templates of finger vein, fingerprint, FKP and finger

shape traits as a single secure sketch. Analyses lead the feature-level fusion for finger multi-

biometric cryptosystem with respect to their impact on security and recognition accuracy.

Comparative experimental results ascertain that finger multi-biometric cryptosystem

outperforms the uni-biometric counterparts in terms of verification performance and

template security. Z.Shariatmadar et al [174] propose an efficient FKP recognition

algorithm based on multi-instance fusion that combines the left index/middle and right

index/middle fingers of an individual at the matching score level. Prior to fusion, a novel

normalization strategy has been applied on each score and a fused score generated for the

final decision by summing the normalized scores. Experimental results on PolyU FKP

database show that the method has an obvious performance improvement compared with

the single-instance method and different normalization strategies.

K.Faez et al [175] present a multimodal biometric identification system based on new

features extraction of palm and ear and describe a biometric approach to personal

identification using robust pattern recognition. Each element of the set has a complex

feature obtained by combining position and scale-tolerant edge detectors over neighboring

positions and multiple orientations. The system‟s architecture has been motivated by a

quantitative model of visual cortex, with fusion applied at the matching-score level. The

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identification process is then divided into four phases: capturing the image, pre-processing,

extracting and normalizing the palm and ear; feature extraction; matching and fusion; and a

decision based on the kNN and SVM classifiers. The system has been tested on a database

of 600 people with 300 palm and 300 ear images. The experimental results show the

effectiveness of the system in terms of the recognition rate. A.Kumar et al [176] proposed a

bimodal biometric fusion system with fusion at feature level. They illustrated the fusion of a

palm texture of a digital camera capture single hand image at feature level. The

demonstrated the discrete cosine transform coefficient basec approach for palm print

recognition. These coefficients can be directly obtained from camera hardware. The results

illustrates that in practice even a small subset of hand shpae and palmprint features are

sufficient for constructing an accurate model for subject identification.A personal

recognition systems using biometrics can be constituted by selecting proper combination of

palmprint features and hand shape. A.Kumar et al [177] proposed a nonlinear rank-level

multi-biometric fusion approach for personal identification. The comparative experimental

results consists of rank-level combination for palmprint matchers using four different

approaches vizBorda count, weighted Borda count, highest and product of ranks, and

Bucklin majority voting along with a nonlinear approach for combining the ranks.

Experimental results carried out on NIST BSSR1 database show significant improvement in

the recognition accuracy with rank-level combinations as compared to individual palmprint.

Also the nonlinear approach simultaneously can improve the FPIR versus FNIR

performance.

Multispectral imaging has been employed with a fusion strategy to acquire more

discriminative information in order to improve the accuracy of palmprint recognition.

PolyU multispectral palmprint images contain four kinds of palmprint images captured

under blue, green, red and near infrared illuminations and contain much information than

the single band. Also there is redundant information and to extract the critical information

among the multispectral palmprint images and to fuse them is an emerging problem. J.Cui

et al [178] addressed the problem of selecting bands from the original four bands by using

the extended general color image discriminant model to generate three new color

components for further improvement of the recognition performance. They have integrated

the extended general color image discriminant algorithm for the color image representation

and recognition. The effectiveness of the models and fusion strategy has been shown

through experiments using the PolyU multispectral palmprints database. The extended

general color image discriminant algorithm can optimally select the spectral bands and is

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able to obtain the best fusion result under the condition that the number of the used spectral

bands is fixed. The general color image discriminant algorithm generates three color

components which contain more information than original color component bands.

Texture and feature based palmprint recognition methods can well exploit the minutiae of

the palmprint but are not very robust to the possible variation such as the rotation and shift

of the palm. Representation based palmprint recognition method can well take advantage of

the holistic information but cannot fully exploit the minutiae of the palmprint. S.Zhang et al

[179] proposed fusion of the competitive coding method and two phase test sample sparse

representation method for palmprint recognition. Two phase test sample sparse

representation method takes the whole palmprint image as the input and determines the

contribution of the training samples of each class in representing the test sample. It also uses

the contribution to calculate the similarities between the test sample and every class.

Competitive coding method is a feature based method and is highly complementary with

two phase test sample sparse representation. To combine the matching scores generated

from two phase test sample sparse representation and the competitive coding method a

weighted fusion scheme has been used. Very high classification accuracy has been obtained

with the method by integrating the global and direction features. A.Kumar et al [180]

investigated comparative performance between Gabor line and appearance based palmprint

representations and using their score and decision level fusion. Product of sum rule achieves

better performance over the fixed combination rules of various representations since the

features from the same image may be correlated. Experimental results on the database of

100 users achieve 34.56% improvement in performance (equal error rate) as compared to

the case when features from single palmprint representation have been employed. Usage of

multiple palmprint representations on the peg free and non contact imaging setup achieves

promising results and demonstrates its usefulness. Best performance can be achieved from

the Gabor filter based representation as compared to the Line or PCA based representations.

Distance measure used to compute the matching distance has important effect on the

performance.A.Kong et al [181] proposed feature level fusion approach for improving the

efficiency of palmprint identification. Phase information on a palmprint image has been

extracted by employing multiple elliptical Gabor filters with different orientations and then

merged according to a fusion rule to form single feature called the Fusion Code.

Normalized hamming distance has been used to measure the similarity of two Fusion

Codes. Final decision has been made using dynamic threshold. Performance of the method

has been validated on database containing 9599 palmprint images from 488 different palms

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and found genuine acceptance rate of 96.33% with execution time about 0.4s. Small sample

recognition problem often leads to unsatisfactory recognition performance in real world

applications. To solve this problem X.Jing et al [182] proposed fusion approach at the

lowest level i.e. the image pixel level by combining face feature (contactless biometric) and

palmprint feature (contacting biometric). Initially they combine the normalized Gabor

transformed face and palmprint images at the pixel level followed by classification to

classify the fused biometric images. Classifier adopted uses nonlinear discriminative feature

extraction approach, kernel discriminative common vectors approach and radial base

function network. Performance evaluation of the algorithm has been carried out with two

largest public face databases (AR and FERET) and a large palmprint database as the test

data. Experimental results demonstrate the effectiveness of the approach for small sample

biometric recognition problem.

T.Savic et al [183] developed a prototype multimodal biometric personal recognition system

based on features extracted from a set of 14 geometrical parameters of hand, palmprint,

digitprint and fingerprint. The features have been extracted from a single high resolution

gray scale image of the palmer surface of the hand using linear discriminant analysis.

Normalized correlation and Euclidean distance has been used for template matching.

Matching scores are normalized using the linear 3-segment normalization technique and

combined using the matcher weighting fusion rule, and finally the best fusion score is

compared with the experimentally determined decision threshold. Optimization of the

system performance for resolution of the images, similarity/dissimilarity measures, match

score normalization and the fusion rule has been determined experimentally. System

configuration showed average equal error rate of 0.0005% with optimum parameters.

R.Raghavendra et al [184] address the problem of designing efficient fusion schemes of

complementary biometric modalities such as face and palmprint that are effectively coded

using Log-Gabor transformation resulting in high dimensional feature spaces. Based on the

FRGC face database of 250 virtual people and PolyU palmprint database for comparison,

different fusion schemes at match score level and feature level have been proposed. A

particle swarm optimization procedure that allows a number of features (identifying a

dominant subspace of the large dimension feature space) to be significantly reduced has

been implemented in order to reduce the complexity of the fusion scheme while keeping the

same level of performance. Significant improvement in performance of 6% has been shown

in both closed identification and verification rates, when performing feature fusion in Log-

Gabor space over the more common optimized match score level fusion method. Due to the

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implementation of a particle swarm optimization algorithm, a reduction in the number of

features by a factor of roughly 45% has been observed while keeping the same level of

performance. J.Aguilar et al [185] presents a modified strategy which combines general user

dependent and general informationin multimodal biometric verification at the decision

level. Bimodal system that uses written signature and fingerprint have been used to compare

user dependent, use independent and adapted fusion and decision schemes. With availability

enough training data for the trained approaches, various experimental findings have been

obtained. Trained fusion/decision outperforms non-trained simple approaches such as sum

rule. Local learning of the fusion functions outperforms localized trained decisions on

summed scores, for the same amount of training data. Local learning outperforms global

learning. Adapted learning using both global information from a pool of users and user

specific training data outperforms all other approaches. When comparing the trained to the

not trained and the global to the local approaches, the issue of critical training data has been

reported. It has been found better to exploit the available information for training the fusion,

instead of the use of existing information for post-fusion trained decisions.

L.Nanni et al [186] study the usefulness of multi-resolution analysis for the face and palm

authentication problems. Different wavelets have been employed to decompose the images

into frequency sub bands with different levels of decomposition. Wavelet coefficients

extracted from some selected sub bands of several wavelet families have been adopted as

features for the authentication. A multi-matcher, formed by combining matchers using the

max rule, has been trained using a single sub band. Sequential forward floating selection has

been used for band selection. Several linear subspace projection techniques have been tested

and compared. Experiments carried out on several biometric datasets show that the

application of Laplacian low equal error rate has been obtained by employing Eigen maps

on a little subset of wavelet sub bands chosen by sequential forward floating selection.

Z.Le-qing et al [187] present a multimodal biometric identification system using geometry

of finger, knuckle print and palm print features of the human hand. A digital camera

captures hand image and preprocesses to get the finger and palm region of interest. The

finger region of interest has been used to extract finger geometry features and knuckle print

features like index, middle, ring and little fingers. Key points and their local descriptors

extracted from palm region of interest represent palm print features. For efficient hand

recognition in a large database, a coarse-to-fine hierarchical method has been employed to

match multiple features. For guided matching in a large database multiple features, namely

Level-1 finger geometry feature, Level-2 knuckle print feature, Level-3 palm print feature,

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have been extracted from a hand. The combination of three levels of features possesses a

large variance between different classes while maintaining a high compactness within the

class. To reduce the number of samples significantly for further processing at the fine level,

coarse-level classification by Level-1, Level-2 features is effective and essential. Palm print

matching implements a high precision verification at Level-3. AND rule fusion shows

improvement of the combined scheme at the decision level. G.Michael et al [188] propose

contactless palm print and knuckle print tracking approach to automatically detect and

capture these features from low-resolution video stream. The encoding of palm print feature

in bit string representation is performed using simple and robust directional coding

technique. Speedy template matching and effective template storage and retrieval is

achieved using bit string representation. Ridgelet transform has been used for extraction of

knuckle print features without resizing the knuckle print images to standard size. Support

Vector Machine has been used for fusing the palm print and knuckle print output scores.

Support Vector Machine with Radial Basis Function kernel has been deployed to fuse the

palm print and knuckle print modalities to yield promising result.

Large numbers of individuals, small sample size and high dimensionality are three major

characteristics of data used in biometrics authentication technologies. Single sample

biometrics recognition faced in real-world applications leads to bad recognition result. To

solve this problem, Y.Yao et al [189] present an approach based on feature level biometrics

fusion by combining two kinds of biometrics namely face as contactless biometrics and

palmprint as a typical contact biometrics. The discriminant feature has been extracted using

Gabor-based image preprocessing and principal component analysis techniques followed by

design of a distance-based separability weighting strategy to conduct feature level fusion.

Large face database (AR database) and a large palmprint database (PolyU database) as the

test data show that the presented approach significantly improves the recognition effect of

single sample biometrics problem and also there is strong supplement between face and

palmprint biometrics. Y.Xu et al [190] define the crossing matching score of two biometrics

traits and combine it with the conventional matching scores for performing personal

authentication. The method is suitable for the bimodal biometrics systems with two similar

biometrics traits such as the system with visible light and infrared face images and the

system with palm images captured at two bands. Initially, the method runs respectively for

the first and biometrics traits. Proposed method calculates the matching scores between the

testing sample and each training sample, for each of these two biometrics traits. The first

and second matching scores are the matching scores generated from the first and second

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traits, respectively. The proposed method then calculates the cross matching scores that are

the matching scores between the testing sample of the second biometrics trait and the

training samples of the first biometrics trait. A weighted fusion scheme finally to combines

the first, second and crossing matching scores for personal authentication. Y. Xu et al [191]

propose a sparse representation method for bimodal biometrics. Initially the method

accomplishes the feature level fusion by combining the samples of the two biometric traits

into a real vector in advance. The method then considers that an approximate representation

of the test sample to be more useful for classification and uses the approximate

representation for classification of sample under test. To produce the approximate

representation of the test sample as basis for classification, the proposed method exploits a

weighted sum of the neighbors from the set of training samples of the test sample. Higher

accuracy has been achieved as demonstrated by the experimentation. D.Zhang et al [192]

present an online personal verification system by fusing palmprint and palmvein

information. Palmvein refers to the palm feature under near-infrared spectrum while

palmprint verification has achieved a great success. Specially designed devices have been

used for capturing palmprint and palmvein features simultaneously to increase robustness,

accuracy and anti-spoof capability of palm based biometric techniques. A dynamic fusion

technique adaptive to image quality has been developed to match the varying quality of the

palmvein image. Liveness detection method based on the image property has been

developed to enhance anti-spoof capability of the system. Higher accuracy has been

achieved by fusing palmprint and palmvein as they contain complementary information.

The system proves to be real time as the verification procedure has been completed in 1.2s

with EER only about 0.0158%. Multi biometrics addresses the challenges such as

unacceptable error rates, intra-class variations and noisy data. M.Heenaye et al [193]

implement a hand vein biometric comprising of dorsal and palmer vein to improve the

accuracy of biometric system using Multi biometrics. Initially, individual scores have been

generated by the individual matchers and used for testing the biometric system. Using score

level fusion that is easy to access and combine the scores obtained from the different

modalities, these scores have been fused. Performance comparison with unimodal biometric

provides better FAR and FRR.Mahesh P. et al [194] propose a multimodal biometric system

using two modalities namely palmprint and speech. The recognition performance has been

increased by integrating the palmprint and speech features. The discriminant features have

been extracted using a modified canonical form method for the palmprint and the Mel

Frequency Cepstral Coefficients technique for speech. Final decision is based on fusion at

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the matching score level. The experimental results on large database show significant

improvement in reliability of recognition systems and demonstrate increases in the

recognition rate.

One of the primary factors affecting the performance of a biometric authentication system is

resolution of the acquired biometric image. Powerful feature extraction is essential for low

quality images. Legendre polynomial has been used for translation and scale invariant

Legendre moments but does not yield rotational invariant form. L.Deepika et al [195]

propose a palmprint verification system in which the 2D Legendre moments are represented

as a linear combination of geometric moment invariants. The modified Legendre moments

have been used for feature extraction. A weighted fusion technique has been used to fuse

the matching scores of the sub-images. Baye‟s classifier based results indicate prediction

accuracy of 98% and hence validating the choice of low order Legendre moment for

effective palmprint verification. A. Mansoor et al [196] present identification scheme based

on palmprint whichmakes use of the textural information obtainable from the palmprint by

using a feature level fusion of non-sub sampled contourlet transform and contourlet

transform. Local and global details in a palmprint have been captured as compact fixed

length palm code. Two-dimensional spectrum has been divided into fine slices using

iterated directional filter banks after establishment of the region of interest. Sub band

outputs for two transforms are separately computed by finding the directional energy

component for each block. The fusion of features from both domains is performed at feature

levels. Normalized Euclidean distance classifier has been used for matching palmprint. The

algorithm is tested on 7752 palm images of PolyU database and 500 palm images of GPDS

Hand database(University of Las Palmas de Gran Canaria). On palm of PolyU database,

contourlet transform based approach confirmed the decidability index of 2.7734 and equal

error rate of 0.2333% whereas non-sub sampled contourlet transform based approach has

shown decidability index of 2.8125 and EER of 0.1604%. On GPDS hand database,

contourlet transform based approach demonstrated the decidability index of 2.6212 and

equal error rate of 0.7082% while non-sub sampled contourlet transform based approach

has shown decidability index of 2.7278 and equal error rate of 0.5082%. Feature fusion

based multimodal approach achieved decidability index of 2.8914 and equal error rate of

0.1563% on PolyUdatabase and decidability index of 2.7956 and equal error rate of

0.3112% on GPDS hand database.A.Lumini et al [197] presented a detailed study of fusion

policies for iris and fingerprint for personal identification. They investigated whether the

integration of fingerprint and iris can attain performance which is not being possible using a

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single biometric.Furthermore they evaluated the correlation among the best algorithms for

verification of fingerprint presented at FVC2004. The results obtained by FVC2004

competitors are combined with IRIS matcher in terms of EER areconsiderably lower than

for other approaches. Itpoints tothe intrinsic error of the system found to be very low and

tending towards 0 in some cases. These results verify that a multimodal biometric

overcomes some of the limitations of a single biometric with a substantial improvement in

performance. H.Mahafzah et al [198] have proposed the use of multi-instance as a mean to

improve the performance of FKP verification. Local orientation information used as FKP

features have been extracted using a log-Gabor filter. Experiments performed using the FKP

database consisting of 7920 images indicate that the Multi-instance verification approach at

Score Level (Max Rule) and Decision Level (OR Rule) outperforms that using any single

instance. At Score Level (Min Rule) and Decision Level (AND Rule) there is no

performance improvement. Using multiple instance of biometric collected using single

sensor has good security level. The degree of improvement in accuracy by fusing multiple

instances is marginal since different instance of the same trait produced the same redundant

features. G.Gao et al [199] describe finger knuckle print (FKP) for personal authentication.

One of the advantages of FKP verification lies in its user friendliness in data collection. The

user flexibility in positioning fingers leads to a certain degree of pose variations in the

collected query FKP images. Gabor filtering based competitive coding scheme has been

sensitive to such variations, resulting in many false rejections. To alleviate such a problem

by reconstructing the query sample with a dictionary learned from the template samples in

the gallery set has been proposed. The reconstructed FKP image can reduce much the

enlarged matching distance caused by finger pose variations simultaneously reducing both

the intra-class and inter-class distances. A score level adaptive binary fusion rule to

adaptively fuse the matching distances before and after reconstruction has been proposed

and aims at reduction of the false rejections without increasing much the false acceptances.

Experimental results on PolyU FKP database show that the method significantly improved

the FKP verification accuracy.

The preceding work on multimodal biometric fusion focused on different techniques for

feature extraction. Use of wavelet filter based on Log-Gabor is proposed by O.Aly et al

[200] to extract the features of palm and face. Features extracted from face and palms are

fused with finger knuckle points at the match level. Gabor wavelet transform technique

based feature extraction method is proposed by Y.Chin et al [201]. The features of

palmprint and fingerprint are fused at feature level. The features extracted using the Gabor

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wavelet filter depends on scale, variance and frequency causing large number of feature

sets. Therefore, accuracy of matching is compared for different length of features at the

fusion level and concluded that more number of features are required for higher accuracy.

D.Sharma et al [202] utilized Log-Gabor filter and Local Binary Pattern to extract and to

fuse the features of Iris and face at feature level. However, due to high redundancy in the

Log-Gabor filter, particle swam optimization method is used to achieve proper Gabor filter

with respect to the modality in [203]. The extracted features are fused at the match level by

normalizing the 1D Log-Gabor filter [204]. They achieved 1.569% FRR and 0.012% FAR.

Three main disadvantageshas been observed for the Gabor based wavelet transform: (1) It is

shift variance, (2) It has poor directionality and (3) It do not contain any phase information.

Iris and ear based multimodal biometric system is proposed by M.Nadheen et al [205]. PCA

method is used to extract the features of each modality and match level fusion is used with

sum operator. They achieved95% accuracyLDA and PCA methods are used to reduce the

dimensions of Gabor filter extracted features and feature level fusion is used

[206].M.Fahmy et al [207] usedSVM for fusionof the fingerprint and iris at match level.

According to them normalization process is required to get better accuracy. The match level

fusion of face and iris is proposed in [208]. This method uses Euclidean distance based

phase of iris and Laplacian face.Match level fusion of DNA, Iris and Face is proposed by

Dinerstein et al [209]. They derived appropriate features from multiple SVM and used them

in fusion process. S.Geethu et al [210] also proposed a match level fusion technique for

fingerprint and iris using multiple SVM. SVM has good generalization properties SVM

found to be more accurate for small training sample sets.Its major limitations are the time

required for training time and parameter selection difficulty [211].

Zernike moment projects 2D image using orthogonal basis function. The Zernike moment

magnitude is invariant to the translation, rotation and scaling. Hence it has powerful

potential in extracting the biometric features. High order moments preservemore features.

Using this,palm and finger features are fused [212].Feature level, match level and decision

level fusion are used with respect to different parts of the hands. Since this model is based

on the segmentation of palm and finger, the finger touch must be avoided during the

acquisition process make it suitable for the higher recognition rate. Rotation invariance is

required to obtained higher recognition rate for any multimodal biometric, and therefore

Zernike moments have better accuracy. However, the computation time is proportional to

the order of Zernike moments [213].