Enhanced Parameter-Free Diversity Discriminant Preserving Projections for Face Recognition

Abstract

The manifold-based learning methods have recently drawn more and more attention in dimension reduction. In this paper, a novel manifold-based learning method named enhanced parameter-free diversity discriminant preserving projections (EPFDDPP) is presented, which effectively avoids the neighborhood parameter selection and characterizes the manifold structure well. EPFDDPP redefines the weighted matrices, the discriminating similarity matrix and the discriminating diversity matrix, respectively. The weighted matrices are computed by the cosine angle distance between two data points and take special consideration of both the local information and the class label information, which are parameterless and favorable for face recognition. After characterizing the discriminating similarity scatter matrix and the discriminating diversity scatter matrix, the novel feature extraction criterion is derived based on maximum margin criterion. Experimental results on the Wine data set, Olivetti Research Laboratory (ORL); AR (face database created by Aleix Martinez and Robert Benavente); and Pose, Illumination, and Expression (PIE) face databases show the effectiveness of the proposed method.