Learned 3D Shape Representations Using Fused Geometrically Augmented Images Application to Facial Expression and Action Unit Detection

This paper proposes an approach to learn generic multi-modal mesh surface representations using a novel scheme for fusing texture and geometric data . Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture image of the mesh . This new fused modality enables us to learn feature representations from 3D data in a highly efficient manner by simply employing standard convolutional neural networks in a transfer-learning mode . The efficacy of our approach is demonstrated for the tasks of facial action unit detection and expression classification . The extensive experiments conducted on the Bosphorus and BU-4DFE datasets, show that our method produces a significant boost in the performance when compared to state-of-the-art solutions . Our method is both computationally and memory

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Keywords : representations - d - approach - mesh - facial -

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