Facial expression recognition based on adaptive local binary pattern and sparse representation

In this paper, we present an effective method for facial expression recognition (FER), which is based on adaptive local binary pattern (ALBP) and sparse representation. The new algorithm first solves sparse representations on both raw gray facial expression images and adaptive local binary patterns (ALBP) of these images. Then we can obtain the two expression recognition results on both expression features. Finally, the final expression recognition is performed by fusion of the two results by comparing the residual ratios of sparse representations. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrate that the proposed fusion algorithm is much better than other methods such as Linear Discriminant Analysis (LDA) + Support Vector Machine (SVM), Kernel Principal Component analysis (KPCA) + SVM, and the performance also improves obviously compared with the direct sparse representation approach.