A New Supervised Laplacian Eigenmap for Expression Recognition

Abstract

In order to reduce interference of identity information on facial expression recognition, this paper proposes a new supervised Laplacian Eigenmap method. Before dimension reduction, make full use of category information to modify the neighborhood structure of the samples which is in the high dimension space, optimize neighborhood information of the test samples, and then modify the weight matrix, improve facial expression recognition rate. The experiments on JAFFE and Cohn-Kanade expression database show the method proposed is more effective to represent facial expression feature than the Laplacian Eigenmap or supervised laplacianfaces.