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Joint and implicit registration for face recognition

Joint and implicit registration for face recognition. Dr. Peng Li and Dr. Simon J.D. Prince Department of Computer Science University College London {p.li, s.prince}@cs.ucl.ac.uk. 14:00-15:00 Tuesday, 23 June 2009. Face detection. Keypoint registration. Face recognition.

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Joint and implicit registration for face recognition

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  1. Joint and implicit registration for face recognition Dr. Peng Li and Dr. Simon J.D. Prince Department of Computer Science University College London {p.li,s.prince}@cs.ucl.ac.uk 14:00-15:00 Tuesday, 23 June 2009

  2. Face detection Keypoint registration Face recognition Feature extraction The face recognition pipeline Matching Gallery Probe Detected face Original Image Result • Global approaches • Eigenfaces [Turk 1991] • Fisherfaces [Belhumeur 1997] • Local approaches • AAM [Cootes 2001] • ASM [Mahoor 2006] • EBGM [Wiskott 1997] • Distance-based approaches • Fisherfaces [Belhumeur1997] • Laplacianfaces [He2005] • KLDA [Yang2005] • Probabilistic approaches • Bayesian [Moghaddam 2000] • PLDA [Ioffe 2006, Prince 2007]

  3. Face detection Keypoint registration Face recognition Feature extraction The face recognition pipeline Matching …… Gallery Probe Detected face Original Image Result • Extract Gabor jet around each keypoint • Generative probabilistic model • Independent term for each keypoint

  4. Probabilistic model Face detection Keypoint registration Keypoint registration Face recognition Feature extraction Feature extraction Hypothesis 1 H1: We can use the same probabilistic model for registration and recognition. Matching …… Gallery Probe Detected face Original Image Result

  5. Hypothesis 2: Joint Registration x Generic eye Particular eye + + Probe Gallery + + H2: We can use the gallery image to help find keypoints in the probe image.

  6. Hypothesis 3: Implicit Registration Probe tp– keypoint position * + Hidden variable Posteriordistribution H3: We do not need to make hard estimates of keypoint positions.

  7. Outline • Background • Hypotheses • Probabilistic face recognition • Frontal face recognition H1: Same model for registration and recognition H2: Joint registration H3: Implicit registration • Cross-pose face recognition • Conclusion

  8. w1j h1 G(:,1) F(:,1) Image xij h2 w2j mean m F(:,2) G(:,2) w3j h3 F(:,3) G(:,3) Probabilistic linear discriminant analysis (Prince & Elder,ICCV 2007) ij μ Fhi Gwij xij + + + = Noise Signal i - # of identity j - # of image + + = + Independent per-pixel Gaussian noise, e Between-individual variation Within-individual variation

  9. hp hg xg xp wp wg hg xg xp Face recognition by model selection Observed Variables Observed Variables • Xp - Probe image • Xg - Gallery image Pr(xp, xg |Md) Md Hidden Variables Hidden Variables Hidden Variables Hidden Variables • No-Match Choose MAP model Pr(xp, xg |Ms ) Ms • Match wp wg

  10. hp hg xg xp wp wg hg xg xp wp wg Methodology 4: Joint and Implicit registration 3: Implicit registration using probe image alone 2: Joint registration by MAP 1: Find keypoint in probe image alone by MAP tp – keypoint position tp + + Posterior over keypoint position Probe Gallery

  11. Experimental Setting: XM2VTS Database • Dataset • Training: First 195 identities • Test: Last 100 identities • Gallery data: 1st image of 1st session • Probe data: 1st image of 4th session • Feature Extraction: Gabor filter at all possible locations of 13 keypoints

  12. Experiment 1: finding keypoints using recognition model in probe alone • Recognition • First match identification rate • Higher is better • Registration • Average error of all keypoints • Lower is better

  13. Experiment 2: joint registration • Gallery image helps find keypoints in probe image • Localization errors are close to human labelling

  14. Experiment 3: implicit registration • Marginalizing over keypoint position is better than using MAP keypoint position

  15. Experiment 4: joint and implicit registration • Joint and implicit registration performs best. • Comparable to using manually labeled keypoints.

  16. Cross-pose face recognition using tied PLDA model (Prince & Elder, 2007) ijk μk Gkwijk xijk Fkhi + + + = Key idea: separate within-individual and between- individual variance at each pose Data: XM2VTS database: with 90° pose difference. Gallery (frontal face) ↔ Probe (profile face) Feature extraction: Gabor feature for 6 keypoints K – Pose Index • K = 1 FRONTAL IMAGE • K = 2 PROFILE IMAGE

  17. Experiment 5: Cross-pose face recognition and registration • Similar results to frontal face recognition & registration • Comparable to using manually labeled keypoints.

  18. Concluding Remarks • Three hypotheses • Same model for both face registration & recognition. • Joint registration for face recognition • Implicit registration for face recognition • All work well for both frontal & cross-pose face registration & recognition

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