Feature extraction using two-dimensional neighborhood margin and variation embedding
In this paper, we introduce a novel linear discriminant approach called Two-Dimensional Neighborhood Margin and Variation Embedding (2DNMVE), which explicitly considers the modes of variability among nearby images and the discriminating information. To be specific, we construct an adjacency graph to model the intra-class variation, which characterizes the modes of variability of the face images, of the values of face images from the same class, and inter-class variation which encodes the discriminating information, and then incorporate the modes of variability and discriminating information into the objective function of dimensionality reduction. Thus, 2DNMVE is robust to intra-class variation and has better generalization capability on testing data. Experiments on four face databases show the effectiveness of the proposed approach. Crown Copyright (C) 2013 Published by Elsevier Inc. All rights reserved.
variation dimensionality reduction manifold learning 2dpca face recognition