1. Face Recognition Committee Machine Presented by Sunny Tang

2. Outline Motivation Motivation Algorithms Review Algorithms Review Face Recognition Committee Machine (FRCM) Face Recognition Committee Machine (FRCM) Experimental Results Experimental Results Conclusion and Future Work Conclusion and Future Work

3. Motivation Applications in security Applications in security AuthenticationAuthentication IdentificationIdentification Face recognition system with high accuracy Face recognition system with high accuracy

4. Eigenface Application of Principal Component Analysis (PCA) Application of Principal Component Analysis (PCA) PCA: PCA: Find eigenvectors and eigenvalues of covariance matrix C from training images T i :Find eigenvectors and eigenvalues of covariance matrix C from training images T i :

5. Eigenface Face Space Face Space Space formed by the span of eigenvectorsSpace formed by the span of eigenvectors Training & Recognition Training & Recognition Project the images on face spaceProject the images on face space Compare Euclidean distance and choose the closest projectionCompare Euclidean distance and choose the closest projection

6. Fisherface Similar to Eigenface Similar to Eigenface Application of Fisher’s Linear Discriminant (FLD)Application of Fisher’s Linear Discriminant (FLD) FLD: FLD: Minimize inner-class variations and maintain between-class discriminabilityMinimize inner-class variations and maintain between-class discriminability

7. Elastic Graph Matching Based on dynamic link architecture Based on dynamic link architecture Extract facial feature by Gabor wavelet transform as a jet Extract facial feature by Gabor wavelet transform as a jet Face is represented by a graph G consists of N nodes of jets Face is represented by a graph G consists of N nodes of jets Compare graphs by cost function Compare graphs by cost function

8. Support Vector Machine Look for a separating hyperplane H which separates the data with the largest margin Look for a separating hyperplane H which separates the data with the largest margin

9. Support Vector Machine Linearly non-separable data Linearly non-separable data Use of kernel function to map data into a higher dimension:Use of kernel function to map data into a higher dimension: Kernel function:Kernel function: Multi-class classification Multi-class classification “one-against-one”“one-against-one” “one-against-all”“one-against-all”

10. FRCM Mixture of five experts Mixture of five experts Static structure Static structure

11. FRCM Result r(i) Result r(i) Individual expert’s result for test imageIndividual expert’s result for test image Confidence c(i) Confidence c(i) How confident the expert on the resultHow confident the expert on the result Weight w(i) Weight w(i) Average performance of an expertAverage performance of an expert

12. Result & Confidence Eigenface, Fisherface, EGM Eigenface, Fisherface, EGM K nearest-neighbour classifiersK nearest-neighbour classifiers Result:Result: Confidence:Confidence:

13. Result & Confidence SVM SVM One-against-one approach usedOne-against-one approach used For J different classes, J(J-1)/2 SVM are constructedFor J different classes, J(J-1)/2 SVM are constructed Confidence:Confidence:

14. Result & Confidence Neural network Neural network Binary vector of size J for target representationBinary vector of size J for target representation Result:Result: Class with output value closest to 1 Class with output value closest to 1 Confidence:Confidence: Output value Output value

15. Voting Machine Ensemble result, confidence to arrive final result Ensemble result, confidence to arrive final result Weight w(i): Weight w(i): Fixed weight for each expertFixed weight for each expert Score s(i) function: Score s(i) function:

16. Experimental Database ORL Face Database ORL Face Database 40 people40 people 10 images/person10 images/person Yale Face Database Yale Face Database 15 people15 people 11 images/person11 images/person

17. Experimental Results ORL Face database ORL Face database

18. Experimental Results Yale Face Database Yale Face Database

19. Conclusion and Future Work Conclusion Conclusion Use of Committee Machine has improvement in accuracyUse of Committee Machine has improvement in accuracy Future Work Future Work Existing algorithms in FRCM do not perform satisfactorily under various lighting condition. Experts like Illumination Cone may helpExisting algorithms in FRCM do not perform satisfactorily under various lighting condition. Experts like Illumination Cone may help Adopt dynamic structure in committee machineAdopt dynamic structure in committee machine

20. Q & A Thanks!