Regularized Neighborhood Boundary Discriminant Analysis for Facial Expression Recognition

In this paper, we propose an approach which named regularized neighborhood boundary discriminant analysis for facial expression recognition. Our algorithm is based on the linear boundary discriminant analysis (LBDA), which aims to find a optimal projection in order to enhance the ability of classification. A regularized method was executed to remove the singularity of within-class metric matrix. Experiments on JAFEE facial expression database and Cohn-Kanade database show that our proposed method can get better performance than some other methods, such as linear discriminant analysis (LDA), local fisher discriminant analysis (LFDA) and linear boundary discriminant analysis (LBDA).