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TRANSCRIPT
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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
Adapted from Yang and Zhou, 2006
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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.
Adapted from Yang and Zhou, 2006
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Results of Experiments (1)
All fusions are better thansingle impression.
Decision fusions are better
than feature fusions.
Adapted from Yang and Zhou, 2006
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Results of Experiments (1)
All fusions are better thansingle impression.
Decision fusions are better
than feature fusions. Sum rule is better than
voting rule.
Adapted from Yang and Zhou, 2006
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Results of Experiments (2)
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:
Adapted from Yang and Zhou, 2006
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A Novel Fusion Framework
Adapted from Yang and Zhou, 2006
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Results of Experiments (3)
Adapted from Yang and Zhou, 2006
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Results of Experiments (4)
For fixed FAR, new scheme shows lower FRR.
For fixed FRR, new scheme shows lower FAR.
Adapted from Yang and Zhou, 2006
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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.