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Deformable Models (Active Shape Models for Facial Analysis)

Deformable Models (Active Shape Models for Facial Analysis). Petia Radeva (part VIII). Centre de Visió per Computador Universitat Autonoma de Barcelona. Face analysis. Broad range of potential applications Personal identification and access control

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Deformable Models (Active Shape Models for Facial Analysis)

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  1. Deformable Models(Active Shape Models for Facial Analysis) Petia Radeva(part VIII) Centre de Visió per ComputadorUniversitat Autonoma de Barcelona

  2. Face analysis Broad range of potential applications • Personal identification and access control • Low-bandwidth communication for videophone and teleconferencing • Forensic applications including videofit and mugshot recognition • Human-computer interaction • Alertness monitoring • Automated surveillance These applications lead to different CV problems • feature location and tracking • person identification • expression recognition • 3D pose recovery, coding, etc.

  3. Automatic Interpretation of Human Faces using Flexible Models • Contactless human-machine interaction systems need facial analysis (3D pose recovery, image coding, face identification, gender recognition, expression recognition and interpretation). • Complex and highly variable structures due to change in: i) individual appearance, 3D pose, facial expression, illumination = > need of flexible statistical models. The landmarks used for deforming face images and the average shape

  4. Example of training, test and difficult test images Data base of 30 individuals and 690 face images.

  5. Modeling shape Typical training shapes and the efect of the main modes of shape variation • PDM contains 152 points extracted manually in 160 training examples

  6. Modeling shape • 16 shape parameters (eigenvectors) are sufficient to describe any face shape • First 3 parameters reflect variations in the 3D pose, 4th and 6th account for shape variation, 5th changes the expression

  7. Modeling shape Typical training shapes and the efect of the main modes of shape variation • Modeling Shape-Free Appearance - warping and normalizing grey-level 79 parameters can express 95% of the variation

  8. Modeling shape-free Gray-Level Appearance The aim: to model gray-level appearance independently of shape (warping by thin splines technique of Bookstein) The landmarks used for deforming face images

  9. Modeling shape-free Gray-Level Appearance Original and shape-free images Training shape- free images

  10. Modeling shape-free Gray-Level Appearance • The main modes of gray-level variation. • 12 variables explained 95% of the grey-level variation in the training set. • First mode explains 80%

  11. Locating Facial Features • Fitting the shape model with an initial model, dierence between both shapes: • 70% of mean scale, =/- 20 pixels displacement, +/- 12% rotation • Reconstructing faces • Gesture interpretation - classication based on the shape parameters • used 89 landmarks and 9 shape parameters

  12. Modeling Local Gray-Level Appearance Extraction of grey-level profile at a model point • It allows for keeping subtle but important localized effects, swamped in the global shape-free model • More robust interpretation

  13. Calculating the appearance parameters Calculating the appearance parameters for a new face image

  14. Locating Facial Features Defining the new preferred position A* for a model point currently at A Inconsistent shapes are avoided by constraining weights b Robust to 3D pose variation and occlusion

  15. Deforming the Shape Model

  16. Deforming the Shape Model Example of the ASM fitting procedure

  17. Tracking, Coding and Reconstructing Faces Face movement tracking using a flexible shape model.

  18. Tracking, Coding and Reconstructing Faces Examples of tracking and reconstruction of face image se-quences (top row: originals, bottom rows: reconstructions).

  19. Tracking, Coding and Reconstructing Faces Reconstruction of faces images of new individuals (top row: originals, bottom row: reconstructions).

  20. Tracking, Coding and Reconstructing Faces Reconstructing occluded face images (top row: originals, bottom row: reconstructions) (nobody in the training set is wearing glasses!).

  21. Recovering 3D Pose Examples of 3D pose recovery on test images due to the fact that first and third shape mode capture 3D position.

  22. Person identification by Fisher analysis

  23. Facial expression recognition Faces displaying the seven expressions used in the expression recognition experiment.

  24. Facial expression recognition The reconstructed mean expressions for our database.

  25. Gender recognition

  26. ASM for Facial Analysis • ASM applied to facial analysis allows: • 3D Pose recovery • Facial features location • Identifying individuals • Facial expression and gender recognition • A fast and robust approach for a complete analysis of facial images • Drawback: Depends on point choice in the PDM • Need of a complete training set with systematically varied pose, expression, and lighting conditions.

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