Facial Expression Recognition Using Sparse Representation

Facial expression recognition is an interesting and challenging subject in signal processing and artificial intelligence. In this paper, a new method of facial expression recognition based on the sparse representation classifier (SRC) is presented. Two typical appearance facial features, i.e., local binary patterns (LBP) and Gabor wavelets representations are extracted to evaluate the performance of the SRC method on facial expression recognition tasks. Three representative classification methods, including artificial neural network (ANN), K-nearest neighbor (KNN), support vector machines (SVM), are used to compare with the SRC method. Experimental results on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database, demonstrate the promising performance of the presented SRC method on facial expression recognition tasks, outperforming the other used methods. . Key-Words: Sparse representation, compressive sensing, facial expression recognition, local binary patterns, Gabor wavelets representations, artificial neural network, K-nearest neighbor, support vector machines