Using color texture sparsity for facial expression recognition

This paper presents a new facial expression recognition (FER) which exploits the effectiveness of color information and sparse representation. For extracting face feature, we compute color vector differences between color pixels so that they can effectively capture change of face appearance (e.g., skin texture). Through comparative and extensive experiment using two public FER databases (DBs), we validate that our color texture features are suited to the sparse representation for improving FER accuracy. Specifically, our color texture features can considerably improve the recognition accuracy obtained by sparse representation compared with other features (e.g., Local Binary Pattern (LBP)) under realistic recognition conditions (e.g., low-resolution faces). It is also shown that the use of our features can yield high discrimination capability and sparsity, justifying the high recognition accuracies obtained. Further, the proposed FER outperforms five other state-of-the-art FER methods.