fingure print recognition

8/4/2019 Fingure Print Recognition

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Fingerprint Recognition


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Features of

FingerprintsFingerprints are imprints formedby friction ridges of the skin in

fingers and thumbs.Their pattern are permanentand unchangeable on each fingerduring all the life;

They are individual (the probabilitythat two fingerprints are alike is about 1 in

1.9x10^15 )They have long been used for

identification


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Fingerprint Applications

Commercial Government Forensic

Computer Network Logon,

Electronic Data Security,E-Commerce,

Internet Access,

ATM, Credit Card,

Physical Access Control,

Cellular Phones

Personal Digital Assistant,

Medical Records,

Distance Leaning, etc.

National ID card,

Correctional Facilities,Drivers License,

Social Security,

Welfare Disbursement,

Border Control,

Passport Control, etc.

Corpse Identification

Criminal Investigation,Terrorist Identification,

Parenthood determination,

Missing Children, etc.


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Fingerprint Application Functionality

Positive Identification Is this person truly know to the system Commercial applications (network logon)

Desirable: low cost and user-friendly

Large Scale Identification Is this person in the database

Government and Forensic applications (prevent doubledipping; multiple passports)

Desirable: high throughput with little human intervention

Surveillance and Screening

Is this a wanted person

Airport watch list

Fingerprints are not suitable


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Challenges

To design a system that would operate on theextremes of all three axis simultaneously

Accuracy

Scale

Usability

101

105

1010

90% 99% 99.9999%

Unusable

Hard to Use

Easy to use


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Reasons for Accuracy Challenges

Information Limitation Due to individuality, poor presentation, and inconsistent

acquisition

Representation Limitation

Design and choice of representation (features) and quality offeature extraction algorithms (especially for poor qualityfingerprints)

Invariance Limitation

Incorrect modeling of invariant relationships among features


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Fingerprint Individuality EstimationAccuracy; Information Limitation

Assumptions for theoretical individuality estimation consider only minutiae (ending and bifurcation) features

minutiae locations and directions are independent

minutiae locations are uniformly distributed

correspondence of a minutiae pair is an independent event quality is not explicitly taken into account

ridge frequency is assumes to be constant across population and

spatially uniform in the same finger

analysis of matching of different impressions of the same finger

binds the parameters of the probability of matching prints from

different fingers

an alignment between two fingerprints has been established


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Information Limitation: ConclusionAccuracy; Information Limitation

There is an incredible amount of information content

in fingerprints

A minutiae-based fingerprint identification system candistinguish between identical twins

The performance of state-of-the-art automatic

fingerprint matchers do not even come close to thetheoretical performance

Performance of fingerprint matcher is depended onthe fingerprint class and thus may depend upon

target population Fingerprint classification may not be very effective in

genetically related population

Fingerprint identification accuracy may suffer in

certain demographics


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Fingerprint RepresentationAccuracy; Representation Limitation

Ideal representation would maximize the inter-classvariability and minimize the intra-class variability

Fingerprints from the same finger

Minutiae-based representationmay not be most suitable Fingerprints from two different fingers

Ridge feature-based representation

may not be most suitable


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Fingerprint RepresentationAccuracy; Representation Limitation

Quality Index = 0.04False Minutiae=27




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Conventional RepresentationsAccuracy; Representation Limitation

Minutiae-based

Sequential design based on the following modules:Segmentation, local ridge orientation estimation (singularity andmore detection), local ridge frequency estimation, fingerprintenhancement, minutiae detection, and minutiae filtering andpost-processing.

Ridge Feature-based

Size and shape of fingerprint, number, type, and position ofsingularities (cores and deltas), spatial relationship and

geometrical attributes of the ridge lines, shape features, globaland local texture information, sweat pores, fractal features.


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Representations: Future DirectionsAccuracy; Representation Limitation

Improvement of current representations through robustand reliable domain-specific image processingtechniques such as:

Model-based orientation field estimation

Robust image enhancement and masking

New richer representations

Fusion of various representations


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Fingerprint InvarianceAccuracy; Invariance Limitation

Ideal matcher would perfectly model the invariant

relationship in different impressions of the samefinger

Two good quality fingerprint images from the same fingerA fingerprint matching algorithm that assumes a rigid transformation will be unable to match


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Matching: Future DirectionsAccuracy; Invariance Limitation

Alignment remains a difficult problem develop

alignment techniques that remain robust under thepresence of false features

Understand and model fingerprint deformation

Fusion of various matchers (based on the same ordifferent representations)


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Scale

1:N Identification is a much harder problem (N large)

Accuracy Speed

Traditionally: classify fingerprint into one of the few (4 orso) predefined fingerprint types

Problem: too few distinct bins; uneven naturaldistribution into these bins; many ambiguousfingerprints (17% NIST4 has two labels)

a) b) c)


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Scale: Future Directions

Continuous classification

Feature-based indexing (search and retrieval) schemes(e.g., minutiae triplets)

Fast matchers

Classifier combination

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30 35 40Error (%)

Penetr

ation

(%)minutiae triplets

orientation image

FingerCode

Combination


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Multiple Biometrics; Fusion

A decision (and lower) level fusion of multiple

biometrics can improve performance

In identification systems, fusion can also improvespeed

Independence among modalities is key

Even combination of correlated modalities can be noworse than the best performing modality alone

Best combination scheme would be applicationdependent


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Performance Evaluation

Evaluation types: technology, scenario, operational

Dependent on composition of the population(occupation, age, demographics, race), theenvironment, the system operational mode, etc

Ideally, characterize the application-independent

performance in laboratory and predict technology,scenario, and operational performances

Standardization and independent testing

Parametric and non-parametric estimation ofconfidence intervals and database size

Parametric and non-parametric and statisticalmodeling of inter-class and intra-class variations;


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Usability, Security, Privacy

Biometrics are not secrets and not revocable

Encryption, secure system design, and livenessdetection solve this problem

Unintended functional scope; unintended application

scope; covert acquisition Legislation; self-regulation; independent regulatory

organizations

Biometric Cryptosystems: fingerprint fuzzy vault

Alignment

Similarity metric in encrypted domain

Variable and unordered representation

Performance loss; ROC remains the bottleneck


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THANK YOU

fingure print recognition

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