Orthogonal Discriminant Neighborhood Preserving Projections for Face Recognition

Subspace learning is one of the main directions for face recognition. In this paper, a novel subspace learning approach, called Orthogonal Discriminant Neighborhood Preserving Projections (ODNPP), is proposed for robust face recognition. The aim of ODNPP is to preserve the within-class geometric structure, while maximizing the between-class scatter. In order to improve the discriminating power, Schur decomposition is used to obtain the orthogonal basis eigenvectors. Experiment results on ORL face database and Yale face database demonstrate the effectiveness and robustness of the proposed method.