Sparse coding-based representation of LBP difference for 3D/4D facial expression recognition

This paper presents an effective method for automated 3D/4D facial expression recognition based on Mesh-Local Binary Pattern Difference (mesh-LBPD). In contrast to most of existing methods, the proposed mesh-LBPD is based on a unified set of geometric and appearance features of different facial regions. Indeed, multiple features are combined into a compact form using covariance matrices, namely Cov - 3D - LBP. Then, the Cov - 3D - LBP atoms are represented as sparse data combinations. To that end, a Riemannian optimization objective for dictionary learning and sparse coding is used, in order to reduce the complexity of the problem, and the representation loss is characterized via an affine invariant Riemannian metric. In order to prove the effectiveness of the proposed compact combination of geometric and appearance features, we conducted extensive experimental validations on real-world datasets. In fact, obtained results show the capability of the proposed method to significantly outperform, or achieve comparable performances with, the state-of-the-art methods.