Multi Enrolled Sample f V

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A Comparative Study ofCombiningMultiple Enrolled Samples for

Fingerprint Verification

Authors: Chunyu Yang, Jie Zhou

Ting-Shuo Yo

12/18/2006

Seminar Pattern Recognition 2006-2nd Period ICS, UU


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Outline

Introduction

Multi-modal fingerprint verification

Feature fusion

Decision fusion

Combination of feature and decision fusion

Results of experiments Summary


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Introduction

Enrollment

Claimant

Fingerprint verification: 1 to 1

Fingerprint identification: 1 to many

Fingerprint processing

Orientation, binarized, thinned, minutiae


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Fingerprint Processing

Orientation Binarized

ThinnedMinutiae

Adapted from O'Gorman, 1998


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Fingerprint Verification Procedure

Adapted from Yang and Zhou, 2006


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Symbols and Terminology

Minutia: M = {x,y,} (position and orientation)

Impression: F = {Mk | k = 1,2,...,n}

Enrolled impressions: E = {Fi | i = 1,2,...,m}

Claimant : C = {F0}

Threshold : Th

Performance

FAR : false acceptance rate

FRR : false rejection rate


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ROC Curves

Adapted from O'Gorman, 1998


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Multimodal FingerprintVerification

Multiple features (different feature representations)

Multiple matchers (different matching algorithms)

Multiple fingers

Multiple impressions

Data level: to fuse multiple impressions of a finger intoa new fingerprint image. (unfeasible)

Feature fusion

Decision fusion


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Feature Fusion



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Feature Fusion (2)

Synthesized template: T = f(E)

Matching phase: match (C,T)

4 Steps:

1. Initialize T = F1, F1: largest similarity to all impressions

2. Align all otherFi with F1

3. Match minutiae, and count confidence ck

4. For each minutia, calculate the total times itsposition appears in effective region, qk

Keep minutia only if ck / qk >


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Feature Fusion (3)

Impression M1

M2

... Mk Mk1 ...

F1

1 1 1...1 1 0 0. ..0

F2

1 0 10 0... 0 1 1 0 1. ..

. . . ... . . ...

. . . ... . . ...

. . . ... . . ...

Fm

1 1 1 1 0 01 0. ..

ck

qk


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Decision Fusion

Match claimant with each impression, andsynthesize the decision.

Probability based

Neyman-Pearson

Similarity based A numeric similarity score.


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Decision Fusion (2)

Sum rule

Take average upon likelihood ration / similarity.

Voting rule Make final decision based on the majority of each

matching result.


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Database and Experiments

Database:

THU, FVC 2002 DB1 and DB2

Each finger: 8 impression (training/testing = 6/2)

Number of impressions

Different extraction/matching algorithms

Different fusion schemes


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Results of Experiments (1)

All fusions are better thansingle impression.



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Decision fusions are better

than feature fusions.



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Decision fusions are better

than feature fusions. Sum rule is better than

voting rule.



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Advantage of feature fusion:

It can be done in registration stage, and hence onlyone fingerprint is matched. ( less computation )

Only one template is saved. ( less storage ) Feature fusion and decision fusion are

complementary to each other:



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A Novel Fusion Framework



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For fixed FAR, new scheme shows lower FRR.

For fixed FRR, new scheme shows lower FAR.



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Conclusions

Decision fusion schemes are better thanfeature fusion schemes.

Among decision fusion strategies, sum ruleoutperforms voting rule.

The proposed novel fusion scheme performsvery well.


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Summary

Fingerprint Processing

Fingerprint Verification Procedure

ROC Curves Feature Fusion

Decision Fusion

A Novel Fusion Framework


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Question?


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Test Question

Which two level of fusions for multipleimpressions are discussed in this research?

What performance measure is used in thisstudy? Give an example that algorithm Aperforms better than B with this measure.

Multi Enrolled Sample f V

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