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How to Control Acceptance Threshold for Biometric Signatures with Different Confidence Values?. Yasushi Makihara ( 槇原 靖 ) , Md. Altab Hossain , Yasushi Yagi ( 八木 康史 ) 大阪大 学 ICPR 2010. Introduction. Biometrics-based verification Quality measure False Acceptance Rate(FAR) - PowerPoint PPT Presentation
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HOW TO CONTROL ACCEPTANCE THRESHOLD FOR BIOMETRIC SIGNATURES WITH DIFFERENT CONFIDENCE VALUES?Yasushi Makihara( 槇原 靖 ) , Md. Altab Hossain, Yasushi Yagi( 八木 康史 )
大阪大学ICPR 2010
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INTRODUCTION
Biometrics-based verification Quality measure
False Acceptance Rate(FAR) False Rejection Rate(FRR) Receiver Operating Characteristics (ROC) curve
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Receiver Operating Characteristics (ROC) curve
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ROC curve
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ROC curve
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Simplified example High confidence (right side) Low confidence(left side)
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Simplified example High confidence (right side) Low confidence(left side)
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FAR FRR
Error rate Acceptance rate
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Gradient
Lower error gradient accepted samples are positive samples
Higher error gradient accepted samples are negative samples
Middle error gradient positive and negative samples in the accepted
samples are balanced
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Implementation
(distance, quality measure)
Weight (ith positive sample for kth quality measure control point)
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Implementation Gaussian kernel-based non-parametric PDF
estimation
Optimal approximation coef. of regularization term
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EXPERIMENTS
Test data
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EXPERIMENTS
Simulation data
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CONCLUSION & DISCUSSION
Outperforms the previous methods in terms of the ROC curve, particularly under a lower FAR or FRR tolerance condition
With the assumption that distributions of distance and quality measures are consistent in the training and test sets, the optimality is not guaranteed in case where the distributions are in consistent.
