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In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation. We describe the temporal evolution of facial landmarks as parametrized trajectories on the Riemannian manifold of positive semidefinite matrices of fixed-rank. Our representation has the advantage to bring naturally a second desirable quantity when comparing shapes – the spatial...
A solution for identity and facial expression recognition is proposed using a two stage classifier approach using low dimensional representation of the geometry of the face. Face geometry is extracted from input images using Active Appearance Models (AAM) and low dimensional manifolds were then derived using Laplacian Eigen-Maps (LE) resulting in two types of manifolds, one for model identity and...
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