Sparse locality preserving discriminative projections for face recognition

Recently, the construction of intrinsic graph using sparse representation (SR) has attracted considerable interest. Comparing with the traditional construction methods like k-NN and ε-ball which can well preserve the manifold structure of samples, SR method is more robust to data noise and parameter-free. To exploit the merits of robustness of sparse representation and manifold learning, we propose a new algorithm called sparse locality preserving discriminative projections (SLPDP), which utilizes sparse representation to construct the intrinsic weighted matrix of training samples and incorporates “locality” and “sparsity” into objective function. Simultaneously, SLPDP takes into account the global information of samples like LDPD and DSNPE, and integrates maximum margin criterion (MMC) into the optimal functions for dimensionality reduction. Experiments on PIE, AR, Extended Yale B and Yale face image databases demonstrate the effectiveness of the proposed approach.