Two-dimensional margin, similarity and variation embedding

Previous works have demonstrated that manifold-based learning discriminant approaches can improve the face recognition accuracy. However, they ignore the variation among nearby face images from the same class, which is important to further improve the recognition accuracy and avoid the over-fitting problem in discriminant approaches. To avoid this problem, we propose a novel approach for face recognition. In our proposed approach, we construct two adjacency graphs to model the margin and information including similarity and variation of face images from the same class, respectively, and then incorporate the information and margin into the dimensionality reduction function. Experiments demonstrate the effectiveness of our approach.