Face Recognition Committee Machine

1. Face Recognition Committee Machine Term Three Presentation by Tang Ho Man

2. Outline • Introduction • Algorithms Review • Face Recognition Committee Machine (FCRM) • Distributed Face Recognition System (DFRS) • Experimental Results • Conclusion and Future Work • Q & A

3. Introduction • Applications in security • Authentication • Identification • Authentication measures • Password • Card/key • Biometric

4. Introduction • Face Recognition • Training phase • Recognition phase • Objectives • Comparison of different algorithms • Face Recognition Committee Machine • Distributed Face Recognition System

5. Review • Algorithms in Committee Machine • Eigenface • Fisherface • Elastic Graph Matching (EGM) • Support Vector Machine (SVM)

6. Review – Eigenface • Application of Principal Component Analysis (PCA) • Find eigenvectors and eigenvalues of covariance matrix C from training images Ti: • Training & Recognition • Project the images on face space • Compare Euclidean distance and choose the closest projection

7. Review – Fisherface • Similar to Eigenface • Application of Fisher’s Linear Discriminant (FLD) • Minimize inner-class variations and maintain between-class discriminability • Projection finding • Between class scatter • Within class scatter • Projection

8. Review – EGM • Based on dynamic link architecture • Extract facial feature by Gabor wavelet transform as a jet • Face is represented by a graph G consists of N nodes of jets • Compare graphs by cost function • Edge similarity • Vertex similarity • Cost function

9. Review – SVM • Look for a separating hyperplane H which separates the data with the largest margin • Decision function • Kernel function • Polynomial kernel • Radial basis kernel • Hyperbolic tangent kernel

10. FRCM - Overview • Mixture of five experts • Eigenface • Fisherface • EGM • SVM • Neural network

11. FRCM - Overview • Elements in voting machine • Result r(i) • Individual expert’s result for test image • Confidence c(i) • How confident the expert on the result • Weight w(i) • Average performance of an expert

12. FRCM - Result & Confidence • Eigenface, Fisherface, EGM • Use K nearest-neighbour classifiers • Five nearest training set images are chosen • Count number of votes for each recognized class • Result • Confidence

13. FRCM - Result & Confidence • SVM • One-against-one approach with maximum voting used • For J different classes, J(J-1)/2 SVM are constructed • Confidence: • Neural network • Binary vector of size J for target representation • Result: • Class with output value closest to 1 • Confidence: • Output value

14. FRCM - Voting Machine • Ensemble results, confidences from experts to arrive a final result • Score function: • Final result – Highest score class • Advantages • High performance • High confidence

15. DFRS • Motivation • Real face recognition application • Face recognition on mobile device • Consists of • Face Detection • Face Recognition

16. DFRS - Limitations • Memory • Little memory for mobile devices • Requirement for recognition • Processing power

17. DFRS - Overview • Client-Server approach • Client • Capture • Ensemble • Server • Recognition

18. DFRS - Testing • Implementation • Desktop (1400MHz) • Notebook (300MHz)

19. ORL Face Database 40 people 10 images/person Yale Face Database 15 people 11 images/person Experimental Results - Database

20. Experimental Results - ORL • ORL Face database

21. Experimental Results - Yale • Yale Face Database

22. Conclusion and Future Work • Conclusion • Comparison of different algorithms • Committee machine improves accuracy • Feasible on mobile device • Future Work • Use of dynamic structure • Include more expert in the committee machine • Implementation on PDA/Mobile

23. Question & Answer Section Thanks!