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This paper presents a graphical model for deformable face matching and landmark localization under an unknown non-rigid warp. The proposed model learns and combines statistics of both appearance and shape variations of facial images (learnt purely from a set of frontal training images) in a complex objective function in an unsupervised manner. Local and global shape variations are included in the objective function as binary and higher order clique potentials. The proposed approach exploits the sparseness of facial features to reduce the complexity of inference over the probabilistic model.
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