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Face Recognition Committee Machine

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  1. Face Recognition Committee Machine Presented by Sunny Tang

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

  3. Motivation • Applications in security • Authentication • Identification • Face recognition system with high accuracy

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

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

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

  7. Elastic Graph Matching • 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

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

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

  10. FRCM • Mixture of five experts • Static structure

  11. FRCM • 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. Result & Confidence • Eigenface, Fisherface, EGM • K nearest-neighbour classifiers • Result: • Confidence:

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

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

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

  16. ORL Face Database 40 people 10 images/person Yale Face Database 15 people 11 images/person Experimental Database

  17. Experimental Results • ORL Face database

  18. Experimental Results • Yale Face Database

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

  20. Q & A Thanks!