Hierarchical Sparse and Collaborative Low-Rank representation for emotion recognition

In this paper, we design a Collaborative-Hierarchical Sparse and Low-Rank (C-HiSLR) model that is natural for recognizing human emotion in visual data. Previous attempts require explicit expression components, which are often unavailable and difficult to recover. Instead, our model exploits the low-rank property to subtract neutral faces from expressive facial frames as well as performs sparse representation on the expression components with group sparsity enforced. For the CK+ dataset, C-HiSLR on raw expressive faces performs as competitive as the Sparse Representation based Classification (SRC) applied on manually prepared emotions. Our C-HiSLR performs even better than SRC in terms of true positive rate.

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