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On the relevance of facial expressions for biometric recognition

On the relevance of facial expressions for biometric recognition. Marcos Faundez-Zanuy, Joan Fabregas Escola Universitària Politècnica de Mataró (Barcelona UPC) SPAIN. OUTLINE. Biometrics vs. facial analysis Transform methods for face recognition Experimental results Identification

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On the relevance of facial expressions for biometric recognition

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  1. On the relevance of facial expressions for biometric recognition Marcos Faundez-Zanuy, Joan Fabregas Escola Universitària Politècnica de Mataró (Barcelona UPC) SPAIN

  2. OUTLINE • Biometrics vs. facial analysis • Transform methods for face recognition • Experimental results • Identification • Verification • Conclusion JCEE’07 15 Novembre 2007

  3. Biometrics vs. facial analysis (1/2) • Biometric problem goal: a feature extraction insensitive to expression and highly discriminative among individuals. • Expression analysis goal: a feature extraction insensitive to different individual’s variation and highly discriminative among expressions. JCEE’07 15 Novembre 2007

  4. Biometrics vs. facial analysis (2/2) • Text-independent Speaker recognition goal: a feature extraction insensitive to the message content and highly discriminative among individuals. • Speech recognition goal: a feature extraction insensitive to different individual’s variation and highly discriminative among phonemes. • Same feature extraction works well for both!: MELCEPSTRUM JCEE’07 15 Novembre 2007

  5. Goal of this paper • In this paper we use the Japanese Female Facial Expression Database (JAFFE) in order to evaluate the influence of facial expression in biometric recognition rates. • In our experiments we used a nearest neighbor classifier with different number of training samples, different error criteria, and several feature extractions JCEE’07 15 Novembre 2007

  6. 100 WHT/DCT coefficients Distance measure image Pattern recognition system JCEE’07 15 Novembre 2007

  7. The Face Recognition Approaches • Identification (1:N) • Pin-less access • It does not work for large populations • Verification (1:1) JCEE’07 15 Novembre 2007

  8. The relevance of feature extraction • It achieves a reduction on the number of data that must be processed, model sizes, etc., with the consequent reduction on computational burden. • The transformation of the original data into a new feature space can let an easier discrimination between classes (faces). JCEE’07 15 Novembre 2007

  9. Dim. Reduction (10) + IDCT DCT Feature extraction based on DCT • DCT: It is a fast transform that requires real operations and it is a near optimal substitute for the KL transform of highly correlated images. It has excellent energy compactation for images. • Applications: MPEG, JPEG, etc. JCEE’07 15 Novembre 2007

  10. Feature extraction based on WHT • The two-dimensional Hadamard transform pair for an image U of pixels is obtained by the equation: JCEE’07 15 Novembre 2007

  11. Computational burden of KLT, DCT and WHT for images of size N×N JCEE’07 15 Novembre 2007

  12. JAFFE DATABASE JCEE’07 15 Novembre 2007

  13. Experiment conditions JCEE’07 15 Novembre 2007

  14. Experimental results (1/8) JCEE’07 15 Novembre 2007

  15. Experimental results (2/8) JCEE’07 15 Novembre 2007

  16. Experimental results (3/8) JCEE’07 15 Novembre 2007

  17. Experimental results (4/8) JCEE’07 15 Novembre 2007

  18. Experimental results (5/8) JCEE’07 15 Novembre 2007

  19. Experimental results (6/8) JCEE’07 15 Novembre 2007

  20. Experimental results (7/8) JCEE’07 15 Novembre 2007

  21. Experimental results (8/8) JCEE’07 15 Novembre 2007

  22. Experimental results: summaryIdentification rates DCT JCEE’07 15 Novembre 2007

  23. Experimental results: summaryVerification errors DCT JCEE’07 15 Novembre 2007

  24. Experimental results: summaryIdentification rates WHT JCEE’07 15 Novembre 2007

  25. Experimental results: summaryVerification errors WHT JCEE’07 15 Novembre 2007

  26. CONCLUSIONS • We have studied the relevance of facial expressions on biometric systems using two different feature extraction algorithms (DCT and WHT) and two different error criterion (MAD and MSE). • Main conclusions are the following: • facial expression produces a drop on recognition rates (verification and identification applications) • Optimal vector length for biometric recognition seems to be same value than in the absence of facial expression (100 components). This value is quite stable for all the studied scenarios (transform, error criterion and different facial expressions). JCEE’07 15 Novembre 2007

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