Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding

Abstract

Given the nonlinear manifold structure of facial images, a new kernel-based supervised manifold learning algorithm based on locally linear embedding (LLE), called discriminant kernel locally linear embedding (DKLLE), is proposed for facial expression recognition. The proposed DKLLE aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. DKLLE is compared with LLE, supervised locally linear embedding (SLLE), principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), and kernel linear discriminant analysis (KLDA). Experimental results on two benchmarking facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database, demonstrate the effectiveness and promising performance of DKLLE.