Discriminant Sparsity Preserving Analysis for Face Recognition

Sparse subspace learning has drawn more and more attentions recently, however, most of them are unsupervised and unsuitable for classification tasks. In this paper, a new discriminant sparsity preserving analysis (DSPA) method by integrating sparse reconstructive weighting into Fisher criterion is proposed for face recognition. We first get sparsity preserving space spanned by the eigenvectors of sparsity preserving projections (SPP). Then, the optimal projection can be obtained by solving an eigenvalue and eigenvector problem of the between-class scatter matrix in sparsity preserving space. The method not only preserves the sparse reconstructive relationship of the data, but also encodes the discriminant information. Extensive experiments on four face image datasets (Yale, ORL, AR and CMU PIE) demonstrate the effectiveness of the proposed DSPA method.