phd thesis. biometrics science studying measurements and statistics of biological data most relevant...
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Biometrics
Science studying measurements and statistics of biological data Most relevant application: id.
recognition
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Why Facial Biometrics ?
Most intuitive way of identification Socially and culturally accepted
worldwide It may work without collaboration
2006
43.6 %
19.2 %
2001
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Facial Biometrics
Challenges ahead Less accurate than iris and
fingerprint Problems with uncontrolled
environments (illumination, viewpoint…) Best system
AverageFully automatic
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Active Shape Models
Automatic training from examples
User-defined template based on landmarks
Model-based parametrization
Generative models5
T.F. Cootes, C. J, Taylor, D.H. Cooper, J. Graham (1995)Computer Vision and Image Understanding, 61(1):38–59
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
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ASM: Construction of the model
Face outlines based on landmarksShape statistics to learn spatial
relationsTexture statistics for image search
Landmarked Training Set
Local texture statistics
Shape statistics
PDM
IIMs
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Point Distribution Model
1.- The input shapes are aligned to remove scale, translation and rotation effects.
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iv iui i i is u R v t
Image Coordinates Model Coordinates
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Point Distribution Model2.- Principal Component Analysis (PCA) on the
aligned shapes
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(2L)-space representationPCA-space
representation
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Point Distribution Model (PDM)
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• Can determine valid shapes
• Can get closest valid shape
• Introduces a representation error
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ASM: Local Texture Statistics (1)
First order derivatives of the pixel intensity For each landmark Sampled perpendicularly to the contour
1:
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i-th landmark
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ASM: Local Texture Statistics (2) Second order statistics for each landmark
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ASM: Model Matching
1. The average shape is placed on the image, roughly matching the face position
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2. Displacement of each landmark to minimize the Mahalanobis distance to the mean profil
3. Apply shape model restrictions
ASM: Model Matching
Steps 2 and 3 are repeated a fixed number of iterations at different resolutions, increasing detail
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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.
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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
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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