Learning Neighborhood Discriminative Manifolds for Video-based Face Recognition

Abstract. In this paper, we propose a new supervised Neighborhood Discriminative Manifold Projection (NDMP) method for feature extrac-tion in video-based face recognition. The abundance of data in videos often result in highly nonlinear appearance manifolds. In order to ex-tract good discriminative features, an optimal low-dimensional projec-tion is learned from selected face exemplars by solving a constrained least-squares objective function based on both local neighborhood geom-etry and global manifold structure. The discriminative ability is enhanced through the use of intra-class and inter-class neighborhood information. Experimental results on standard video databases and comparisons with state-of-art methods demonstrate the capability of NDMP in achieving high recognition accuracy