Face recognition based on orthogonal discriminant locality preserving projections

Face image data taken with various capturing devices are usually high dimensional and not very suitable for accurate classification. Recently, a lot of manifold learning algorithms have been used in face recognition community. Among them, locality preserving projections (LPP) is one of the most promising feature extraction techniques. In this paper, a new face recognition method based on orthogonal discriminant locality preserving projections (ODLPP) is proposed. Based on LPP, ODLPP takes into account the between-class information, changes the objective function, and then orthogonalizes the basis vectors of the face subspace. The proposed method was compared with eigenface, Fisherface, orthogonal LPP (OLPP) and Laplacianface methods on the Yale and AR face databases. Experimental results indicated the promising performance of the proposed method.