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Face Recognition Committee Machine

Face Recognition Committee Machine. Presented by Sunny Tang. Outline. Motivation Algorithms Review Face Recognition Committee Machine (FRCM) Experimental Results Conclusion and Future Work. Motivation. Applications in security Authentication Identification

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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!

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