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Topic 5. Human Faces

Topic 5. Human Faces. Human face is extensively studied in vision. Depending on the applications, there are a long list of tasks [5]: Detection and Recognition: Face detection (finding all faces in a picture), facial feature detection (eyes, lips, …),

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Topic 5. Human Faces

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  1. Topic 5. Human Faces • Human face is extensively studied in vision. Depending on the applications, there are a • long list of tasks [5]: • Detection and Recognition: • Face detection (finding all faces in a picture), facial feature detection (eyes, lips, …), • Face localization (detecting a single face in image), • Face recognition or identification (from a database, classification) • Face authentication (verifying claim, bank id), Age/gender recognition, • Face tracking (location and pose over time) • Facical expression recognition (affective states), aesthetic study. • Modeling and Photorealistic Synthesis: • Appearance models, deformable templates, lighting models, facial action units, • face hallucination (high resolution from low resolution), • pose adjustment, image editing (removing wrinkles, eye glass, red-eye etc.) • 3. Artistic rendering • Sketch, portrait, caricature, cartoon, painting, …

  2. Face Image Databases The CMU Rowley dataset

  3. Face Image Databases The CMU Schneidrman and Kanade Dataset

  4. References. 1. P. Hallinan, G. Gordon, A. Yuille, P. Giblin, and D. Mumford, 2D and 3D Patterns of the Face, A.K. Peters, Ltd. Book chapters 2-4. (handouts). 2. D.H. Ballard, "Generaling the Hough transform to detect arbitrary shapes", (in handbook). 3. P. Viola and M. Jones, "Robust Real Time Object Detection", 4. F. Fleuret and D. Geman, " Coarse-to-fine face detection", IJCV 41(1/2),2001. 5. M.H. Yang, D. Kriegman, N. Ahuja, “Detecting faces in images, a survey”, PAMI vol.24,no.1, January, 2002. 6 T.F.Cootes, G.J. Edwards and C.J.Taylor. "Active Appearance Models", ECCV 1998 7. C. Liu, S. C. Zhu, and H. Y. Shum, "Learning inhomogeneous Gibbs models of faces by minimax entropy", ICCV 2001. 8. Y. Tian, T. Kanade, and J. Cohn, "Recognizing action units for facial expression analysis" PAMI, Feb, 2001. 9. H. Chen, Y. Q. Xu, H. Y. Shum, S. C. Zhu, and N. N. Zhen, "Example-based facial sketch generation with non-parametric sampling", ICCV 2001.

  5. Outline • We proceed in three steps: • A survey on face detection and recognition techniques • Mathematical models of face images • 3. Face synthesis: photorealistic and non-photorealistic.

  6. Face Detection Methods [5]

  7. Face vs non-face Clsutering 6 clusters in a 19 x19 space (Sung and Poggio)

  8. Distance Measure D2 D1 For each input image, it measures two distances for each cluster center: D1 is the Mahalanobis distance and D2 is the Euclidean distance. Thus Sung and poggio have 2 x 6 x 2 = 24 features for classification in a multiple layer perceptron.

  9. Deformable Face Template Deformable face template by Fishler and Elschlager 1973. M. Fishler and R. Elschlager, “The representation and matching of pictorial structures”, IEEE Trans. on Computer. Vol.C-22, 67-92, 1973.

  10. Local Deformation and Global Transform Geometric variations of faces: (Hallinan, Yuille, Mumford et al)

  11. Deformable Model of Facial Features Eye template using parabolic curves by Yuille et al 1989-92. A.L.Yuille, D. Cohen, and P.Hallinan, “Feature extraction from faces using deformable templates”, CVPR 89, IJCV 92. We can derive meaningful diffusion equations from the energy functionals.

  12. Upper Face Action Units

  13. Lower Face Action Units

  14. Templates for Various States

  15. Templates for Various States

  16. Features for Action Unit Recognition

  17. Classification from Feature Vector

  18. Recognition Rate

  19. Apparence Model: Landmarks on a face 400 images each labeled with 122 points.

  20. Eigen-vectors for Geometry and Photometry

  21. Apparence Model

  22. Face Localization and Recognition

  23. A Linear HMM Model for Face

  24. Face Detection

  25. Sample of the 4D space

  26. Multi-scale Detection

  27. Edge Features

  28. Decision Tree

  29. Examples of Decision Trees

  30. Bounds Analysis

  31. Some Examples

  32. Face Prior Learning: Experimental Details • 83 key points defined on face • 720 individuals with all kinds of types • Dimension reduced to 33 by PCA • 40000 samples drawn by the inhomogeneous Gibbs sampler in each Monte Carlo integration • 50 features pursuit • Total runtime: about 5 days on a PIII 667, 256MB PC

  33. Obs & Syn Samples (1) Observed faces Synthesized faces without any features

  34. Synthesis Samples Synthesized faces with 10 features Synthesized faces with 20 features

  35. Synthesis Samples Synthesized faces with 30 features Synthesized faces with 50 features

  36. 50 Observed Histograms

  37. 50 Synthesized Histograms

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