A minimum-distance discriminant projection (MDP)algorithm is proposed to address face recognition problem. Different from the classical linear discriminant analysis (LDA)

the MDP is a manifold learning based dimensionality reduction algorithm. MDP first defines the intra-class similarity

weight

and the inter-class weight of each sample. The former one can measure the distance between each data point and the intra-class center

while the latter one does not only characterize the distance between the data point and the inter-class center but also can reflect the relation between the between-class distance and the within-class distance. Then

the high-dimensional data is mapped into a low-dimension space such that the points to within-class center distances are minimized while the points to between-class center distances are maximized simultaneously. At last

experiments on the ORL

FERET

and AR face databases show that the proposed algorithm can outperform other algorithms.