1. 1 Performance Evaluation of Score Level Fusion in Multimodal Biometric Systems Web Computing Laboratory Computer Science and Information Engineering Department Fu Jen Catholic University Speaker: Wei Tin Lai Advisor Prof. Hsing Mei

2. Outline Introduction Background RHE Normalization Experiment & Result Conclusion 2

3. Introduction Classical user authentication system –Identification card –Key –Etc.. Biometric-based authentication system –Reliable verification –Reliable identification - Base on … 3

4. Introduction Fingerprint 4 FaceFinger vein

5. Introduction Are these reliable ? 5

6. Introduction Unibiometric system –Noisy data –Lack of distinctiveness of the trait Multimodal biometric system –Combine multiple biometric samples (face, fingerprint..) –Normalization –Fusion 6

7. Introduction 7

8. Outline Introduction Background RHE Normalization Experiment & Result Conclusion 8

9. Background Normalization –Min-Max normalization –max(X):maximum value of the raw matching scores –min(X):minimum value of the raw matching scores 9

10. Background Min-Max normalization –Drawback: Sensitive to outliers Original data After normalization 10

11. Background Z-Score normalization Also sensitive to outliners 11

12. Background Tanh-Estimators normalization Oop…so many parameters... 12 ‧ Too many parameters have to be determined _

13. Outline Introduction Background RHE Normalization Experiment & Result Conclusion 13

14. RHE Normalization Reduction of High-scores Effect (RHE) Normalization Two observations –Normalization will causes loss of information –Suffer mainly from the ‘LOW’ genuine scores instead of ‘HIGH’ imposter scores 14

15. RHE Normalization With these observation, RHE normalization procedure as follows: 1)Use the raw data if the range of data is similar.(Will not normalize the data). 2)Modify the min-max normalization formula to fit the ‘LOW’ genuine scores. 15

16. RHE Normalization RHE normalization formula –X: the distribution of all raw scores. –X*:the distribution of all genuine raw scores Advantage: Performance will be increased. Drawback: ‘low’ impostor scores will also uplifted. 16

17. Sum Rule-based Fusion The fused score(fs) is evaluated using following formula: W: weight In here we set all the weight to be 1 17

18. Sum Rule-based Fusion The fused score(fs) is evaluated using following formula: W: weight In here we set all the weight to be 1 18

19. SVM-based Fusion Support Vector Machines(SVM)-based Fusion Using for classification 19

20. Outline Introduction Background RHE Normalization Experiment & Result Conclusion 20

21. Experiment & Result National Institute of Standard and Technology(NIST) Biometric Score Set(BSSR1) Databases –NIST-Multimodal Face score(Matcher C,Matcher G) Fingerprint(Left index finger,Right index finger) –NIST-Face Face score(Matcher C, Matcher G) 21

22. Experiment & Result Databases(cont.) –NIST-Fingerprint Left index finger Right index finger –Merged database of fingerprint, face and finger vein Face scores(Matcher G) Fingerprint scores(Right index finger) Finger vein 22

23. Experiment & Result Genuine Accept Rate(GAR ) False Accept Rate(FAR) 23

24. Experiment & Result 24

25. Experiment & Result 25

26. Experiment & Result 26

27. Experiment & Result 27

28. Outline Introduction Background RHE Normalization Experiment & Result Conclusion 28

29. Conclusion Multimodal biometric modal has better performance than Unimodal biometric modal SVM fusion is better than Sum rule-based fusion if the parameters are determined RHE has better performance! 29

30. Conclusion 30 Rating_Total Web application

31. Conclusion 31

32. Thank you! 32 Q&A