Facial expression recognition based on Gabor wavelets and sparse representation

The recently-emerged sparse representation in compressive sensing (CS) has gained extensive attention in signal processing and pattern recognition. In this paper, a new method of facial expression recognition based on Gabor wavelets and sparse representation classifier (SRC) is presented. Gabor wavelets representations are firstly 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 the popular JAFFE facial expression databases, demonstrate the promising performance of the presented SRC method on facial expression recognition tasks, outperforming the other used methods.