Facial Expression Recognition Based on Rotation Invariant Local Phase Quantization and Sparse Representation

In this paper, we propose a new algorithm for facial expression recognition (FER) based on rotation invariant Local Phase Quantization (RI-LPQ) and sparse representation. Firstly, Expression features are extracted using RI-LPQ descriptor. Then, Sparse Representation-based Classification (SRC) method is used to represent the test expression image by the linear combination of the training expression images. Facial expressions are discriminated by the residue analysis of sparse representation. The proposed method is experimented on Japanese Female Facial Expression (JAFFE) database. The new algorithms are assessed in comparison with the well known algorithms such as 2DPCA+SVM, LDA+SVM etc. The results illustrate that the proposed method has better performance than those traditional algorithms. In addition, in the case of expression images with different occlusion, recognition rate of the proposed method also gets better result.