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Joint and Implicit Registration Techniques for Enhanced Face Recognition

This paper, presented by Dr. Peng Li and Dr. Simon J.D. Prince at University College London, explores innovative methods in face recognition through joint and implicit registration techniques. The approach combines keypoint registration and feature extraction to improve accuracy in recognizing faces from varying poses. We discuss a probabilistic model that unifies registration and recognition, providing experimental results from the XM2VTS database, highlighting the effectiveness of these techniques compared to traditional methods. Our findings indicate that joint and implicit registration significantly enhance recognition performance.

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Joint and Implicit Registration Techniques for Enhanced 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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