Neighborhood discriminant projection for face recognition

We propose a novel manifold learning approach, called Neighborhood Discriminant Projection (NDP), for robust face recognition. The purpose of NDP is to preserve the within-class neighboring geometry of the image space, while keeping away the projected vectors of the samples of different classes. For representing the intrinsic within-class neighboring geometry and the similarity of the samples of different classes, the within-class affinity weight and the between-class affinity weight are used to model the within-class submanifold and the between-class submanifold of the samples, respectively. Comprehensive comparisons and extensive experiments on face recognition are performed to demonstrate the effectiveness and robustness of our proposed method. (C) 2007 Elsevier B.V. All rights reserved.