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PhD Thesis

PhD Thesis. Biometrics. Science studying measurements and statistics of biological data Most relevant application: id. recognition. Why Facial Biometrics ?. Most intuitive way of identification Socially and culturally accepted worldwide It may work without collaboration. 2001. 19.2 %.

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PhD Thesis

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  1. PhD Thesis

  2. Biometrics • Science studying measurements and statistics of biological data • Most relevant application: id. recognition

  3. Why Facial Biometrics ? • Most intuitive way of identification • Socially and culturally accepted worldwide • It may work without collaboration 2001 19.2 % 43.6 % 2006

  4. Facial Biometrics • Challenges ahead • Less accurate than iris and fingerprint • Problems with uncontrolled environments (illumination, viewpoint…) Best system Average Fully automatic

  5. Automatic training from examples User-defined template based on landmarks Model-based parametrization Generative models Active Shape Models T.F. Cootes, C. J, Taylor, D.H. Cooper, J. Graham (1995) Computer Vision and Image Understanding, 61(1):38–59

  6. This thesis… • Focus on 3 contributions to ASMs on relevant aspects for facial feature localization: • More accurate segmentation invariant to in-plane rotations • Add robustness to out-of-plane rotations • Estimate the Reliability of the segmentation 1 2 3 4

  7. Face outlines based on landmarks Shape statistics to learn spatial relations Texture statistics for image search Shape statistics Local texture statistics 1 ASM: Construction of the model PDM IIMs Landmarked Training Set

  8. 1.- The input shapes are aligned to remove scale, translation and rotation effects. 1 1 Point Distribution Model Model Coordinates Image Coordinates

  9. 2.- Principal Component Analysis (PCA) on the aligned shapes (2L)-space representation PCA-space representation 1 1 Point Distribution Model

  10. 1 1 Point Distribution Model (PDM) • Can determine valid shapes • Can get closest valid shape • Introduces a representation error

  11. More specific More general 1 1 Point Distribution Model (PDM)

  12. 1 1 PDM: Modes of variation Variation from 1st Principal Component

  13. 1 1 PDM: Modes of variation Variation from 2nd Principal Component

  14. First order derivatives of the pixel intensity For each landmark Sampled perpendicularly to the contour 1 1 ASM: Local Texture Statistics (1) i-th landmark

  15. Second order statistics for each landmark 1 1 ASM: Local Texture Statistics (2) i-th landmark

  16. The average shape is placed on the image, roughly matching the face position 1 1 ASM: Model Matching • Displacement of each landmark to minimize the Mahalanobis distance to the mean profil • Apply shape model restrictions

  17. 1 1 ASM: Model Matching Steps 2 and 3 are repeated a fixed number of iterations at different resolutions, increasing detail

  18. 1 1 ASM: Model Matching

  19. 1 1 ASM: Model Matching

  20. 1 1 ASM: Complex textures • Several factors modify facial appearance • beard, hair cut, glasses, teeth. • The distribution of the normalized gradient is often non Gaussian nor unimodal.

  21. 1 1 ASM: Complex textures

  22. Texture description based on Taylor series Grids centered at the landmarks for local analysis Non linear classifier (kNN) for inside-outside labeling outside inside 2 1 Optimal Features ASM B. van Ginneken, A.F. Frangi, J.J. Staal, B.M. terHaarRomeny, and M.A. Viergever (2002) IEEE Transactions on Medical Imaging, 21(8):924–933

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