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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 l.jpg

Deformable Models(Active Shape Models for Facial Analysis)

Petia Radeva(part VIII)

Centre de Visió per ComputadorUniversitat Autonoma de Barcelona


Face analysis l.jpg
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.


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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


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Example of training, test and difficult test images Models

Data base of 30 individuals and 690 face images.


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Modeling shape Models

Typical training shapes and the efect of the main modes of shape variation

  • PDM contains 152 points extracted manually in 160 training examples


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Modeling shape Models

  • 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


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Modeling shape Models

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


Modeling shape free gray level appearance l.jpg
Modeling shape-free Gray-Level Appearance Models

The aim: to model gray-level appearance independently of shape (warping by thin splines technique of Bookstein)

The landmarks used for deforming face images


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Modeling shape-free Gray-Level Appearance Models

Original and shape-free images

Training shape-

free images


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Modeling shape-free Gray-Level Appearance Models

  • The main modes of gray-level variation.

  • 12 variables explained 95% of the grey-level variation in the training set.

  • First mode explains 80%


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Locating Facial Features Models

  • 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


Modeling local gray level appearance l.jpg
Modeling Local Gray-Level Appearance Models

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


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Calculating the appearance parameters Models

Calculating the appearance parameters for a new face image


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Locating Facial Features Models

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



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Deforming the Shape Model Models

Example of the ASM fitting procedure


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Tracking, Coding and Reconstructing Faces Models

Face movement tracking using a flexible shape model.


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Tracking, Coding and Reconstructing Faces Models

Examples of tracking and reconstruction of face image se-quences

(top row: originals, bottom rows: reconstructions).


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Tracking, Coding and Reconstructing Faces Models

Reconstruction of faces images of new individuals (top row:

originals, bottom row: reconstructions).


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Tracking, Coding and Reconstructing Faces Models

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


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Recovering 3D Pose Models

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



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Facial expression recognition Models

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


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Facial expression recognition Models

The reconstructed mean expressions for our database.



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ASM for Facial Analysis Models

  • 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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