Discriminative sparsity preserving embedding for face recognition

Abstract

Over the past few years, sparse representation (SR) becomes a hotspot and applied in many research fields. Sparsity preserving projections (SPP) utilizes SR to dimensionality reduction (DR) for face classification. However, as the original framework of SR is unsupervised, SPP can not employ the class information, which is very crucial for classification. To address this problem, we propose an algorithm, namely supervised SR (SSR), to cooperate with label information. Furthermore, we also propose a DR method, discriminative sparsity preserving embedding (DSPE), in this paper. DSPE learns the discriminative sparse structure with SSR and finds the low dimensional subspace that reduces the within class distances and keeps the between class distances. Compared with the related state-of-the-art methods, experimental results on benchmark face databases verify the advancement of the proposed method.