Facial expression recognition based on tensor local linear discriminant analysis

Linear discriminant analysis (LDA) is an effective method for solving the classification problems. Many based-discriminant analysis approaches have been proposed to extract more discriminant information and try to overcome the limitation of LDA. Local linear discriminant analysis (LLDA) was proposed to capture the local structure of samples, it can overcome the assumption of Gaussian distribution which emerge in traditional LDA. In this paper, we proposed tensor version of LLDA, tensorLLDA not only can avoid the undersampled problem which appear in LDA and LLDA, but also reduce the computation complexity. Experiment on JAFFE facial expression database and Cohn-Kanade facial expression database show the effectiveness of tensorLLDA.