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How to Control Acceptance Threshold for Biometric Signatures with Different Confidence Values?

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)

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How to Control Acceptance Threshold for Biometric Signatures with Different Confidence Values?

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  1. How to Control Acceptance Threshold for Biometric Signatures with Different Confidence Values? VC Lab, Dept. of Computer Science, NTHU, Taiwan Yasushi Makihara(槇原 靖) , Md. AltabHossain, Yasushi Yagi(八木 康史) 大阪大学 ICPR 2010

  2. Introduction • Biometrics-based verification • Quality measure • False Acceptance Rate(FAR) • False Rejection Rate(FRR) • Receiver Operating Characteristics (ROC) curve VC Lab, Dept. of Computer Science, NTHU, Taiwan

  3. Adaptive acceptance threshold control • Receiver Operating Characteristics (ROC) curve VC Lab, Dept. of Computer Science, NTHU, Taiwan

  4. Adaptive acceptance threshold control • ROC curve VC Lab, Dept. of Computer Science, NTHU, Taiwan

  5. Adaptive acceptance threshold control • ROC curve VC Lab, Dept. of Computer Science, NTHU, Taiwan

  6. Adaptive acceptance threshold control • Simplified example • High confidence (right side) • Low confidence(left side) VC Lab, Dept. of Computer Science, NTHU, Taiwan

  7. Adaptive acceptance threshold control • Simplified example • High confidence (right side) • Low confidence(left side) VC Lab, Dept. of Computer Science, NTHU, Taiwan

  8. Adaptive acceptance threshold control • FAR FRR • Error rate Acceptance rate VC Lab, Dept. of Computer Science, NTHU, Taiwan

  9. Adaptive acceptance threshold control • 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 VC Lab, Dept. of Computer Science, NTHU, Taiwan

  10. Adaptive acceptance threshold control • Implementation (distance, quality measure) • Weight (ith positive sample for kth quality measure control point) VC Lab, Dept. of Computer Science, NTHU, Taiwan

  11. Adaptive acceptance threshold control • Implementation • Gaussian kernel-based non-parametric PDF estimation • Optimal approximation coef. of regularization term VC Lab, Dept. of Computer Science, NTHU, Taiwan

  12. Experiments • Test data VC Lab, Dept. of Computer Science, NTHU, Taiwan

  13. Experiments • Simulation data VC Lab, Dept. of Computer Science, NTHU, Taiwan

  14. 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. VC Lab, Dept. of Computer Science, NTHU, Taiwan

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