1. PhD Thesis

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

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

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

5. Active Shape Models Automatic training from examples User-defined template based on landmarks Model-based parametrization Generative models 5 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 6

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

8. Point Distribution Model 1.- The input shapes are aligned to remove scale, translation and rotation effects. Image Coordinates Model Coordinates 11 8

9. Point Distribution Model 2.- Principal Component Analysis (PCA) on the aligned shapes (2L)-space representation PCA-space representation 11 9

10. Point Distribution Model (PDM) Can determine valid shapes Can get closest valid shape Introduces a representation error 11 10

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

12. PDM: Modes of variation Variation from 1st Principal Component 11 12

13. PDM: Modes of variation Variation from 2nd Principal Component 11 13

14. ASM: Local Texture Statistics (1) First order derivatives of the pixel intensity  For each landmark  Sampled perpendicularly to the contour i-th landmark 11 14

15. ASM: Local Texture Statistics (2) Second order statistics for each landmark i-th landmark 11 15

16. ASM: Model Matching 1. The average shape is placed on the image, roughly matching the face position 11 16 2. Displacement of each landmark to minimize the Mahalanobis distance to the mean profil 3. Apply shape model restrictions

17. ASM: Model Matching Steps 2 and 3 are repeated a fixed number of iterations at different resolutions, increasing detail 11 17

18. ASM: Model Matching 11 18

19. ASM: Model Matching 11 19

20. 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. 11 20

21. ASM: Complex textures 11 21

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