1. H OW TO C ONTROL A CCEPTANCE T HRESHOLD FOR B IOMETRIC S IGNATURES WITH D IFFERENT C ONFIDENCE V ALUES ? Yasushi Makihara( 槇原 靖 ), Md. Altab Hossain, Yasushi Yagi( 八木 康史 ) 大阪大学 ICPR 2010 1 VC Lab, Dept. of Computer Science, NTHU, Taiwan

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

3. A DAPTIVE ACCEPTANCE THRESHOLD CONTROL Receiver Operating Characteristics (ROC) curve 3 VC Lab, Dept. of Computer Science, NTHU, Taiwan

4. A DAPTIVE ACCEPTANCE THRESHOLD CONTROL ROC curve 4 VC Lab, Dept. of Computer Science, NTHU, Taiwan

5. A DAPTIVE ACCEPTANCE THRESHOLD CONTROL ROC curve 5 VC Lab, Dept. of Computer Science, NTHU, Taiwan

6. A DAPTIVE ACCEPTANCE THRESHOLD CONTROL Simplified example High confidence (right side) Low confidence(left side) 6 VC Lab, Dept. of Computer Science, NTHU, Taiwan

7. A DAPTIVE ACCEPTANCE THRESHOLD CONTROL Simplified example High confidence (right side) Low confidence(left side) 7 VC Lab, Dept. of Computer Science, NTHU, Taiwan

8. A DAPTIVE ACCEPTANCE THRESHOLD CONTROL FAR FRR Error rate Acceptance rate 8 VC Lab, Dept. of Computer Science, NTHU, Taiwan

9. A DAPTIVE 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 9 VC Lab, Dept. of Computer Science, NTHU, Taiwan

10. A DAPTIVE ACCEPTANCE THRESHOLD CONTROL Implementation (distance, quality measure) Weight ( i th positive sample for k th quality measure control point) 10 VC Lab, Dept. of Computer Science, NTHU, Taiwan

11. A DAPTIVE ACCEPTANCE THRESHOLD CONTROL Implementation Gaussian kernel-based non-parametric PDF estimation Optimal approximation coef. of regularization term 11 VC Lab, Dept. of Computer Science, NTHU, Taiwan

12. E XPERIMENTS Test data VC Lab, Dept. of Computer Science, NTHU, Taiwan 12

13. E XPERIMENTS Simulation data VC Lab, Dept. of Computer Science, NTHU, Taiwan 13

14. C ONCLUSION & D ISCUSSION 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. 14 VC Lab, Dept. of Computer Science, NTHU, Taiwan