Facial Expression Recognition via Kernel Sparse Representation

Abstract

For seeking sparse representation coefficients in the kernel feature space, we propose a novel method of facial expression recognition based on kernel sparse representation classification to improve the recognition rate of facial expression. The 1 l -norm minimization problem was solved in this paper. The coefficient vector was obtained and the local binary patterns features were extracted as the facial representation features. kernel sparse representation classification was used to perform facial expression classification and compared with sparse representation-based classification, the nearest subspace, support vector machines, K-nearest neighbor, and radial basis function neural network. Test results based on the universal JAFFE database show that the presented kernel sparse representation classification method is more effective than other methods in facial expression recognition.