Manifold Aware Discriminant Collaborative Graph Embedding for Face Recognition

Dimensionality reduction has been widely used to deal with high dimensional data. In this paper, based on manifold learning and collaborative representation, an efficient subspace learning algorithm named Manifold Aware Discriminant Collaborative Graph Embedding (MADCGE), is proposed for face recognition. Firstly, the representation coefficients of face images are obtained by collaborative representation combined with label information and manifold structure. Then, it constructs a new graph with the coefficients obtained as the adjacent weights. Lastly, graph embedding is exploited to learn an optimal projective matrix for feature extraction. As a result, the proposed algorithm avoids choosing the neighborhood size of graph, which is difficult in literature. More importantly, it can not only preserve the linear reconstructive relationships between samples, but also sufficiently utilize the merits of label information and nonlinear manifold structure to further improve the discriminative ability. Extensive experiments on face databases (AR face database and YALE-B face database) are conducted to exam the performance of the proposed scheme and the results demonstrate that the proposed method has better performance than some other used methods.