Gabor attributes tracking for face verification

A method based on sequential importance sampling is proposed for tracking facial features on a grid with Gabor attributes. The motion of facial feature points is modeled as a global 2-D affine transformation (accounting for head motion) plus a local deformation (accounting for residual motion due to inaccuracies in 2-D affine modeling and other factors such as facial expression). Motion of both types is estimated simultaneously by the tracker: global motion is tracked by importance sampling, and residual motion is handled by incorporating local deformation into the measurement likelihood in computing the weight of a sample. While it has other applications in facial analysis, the method is particularly applicable to face verification because of a novel parametrization.

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