Minimum-distance discriminant projection and its application to face recognition
最小距离鉴别投影及其在人脸识别中的应用

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.