Two-stage patch-based sparse multi-value descriptor for face recognition

Riqiang Gao, Wenming Yang, Xiaoling Hu, Qingmin Liao

Abstract: In this paper, we propose Two-stage Patch-based Sparse Multi-value Descriptor (TPSMD), a generalization of Sparse Linear Regression Binary method. The TPSMD makes two contributions. First, the multi-value strategy introduces user-specified parameters to improve the binarization, which makes our method more discriminant and less sensitive to noise. The multi-value strategy is a comprise between the simplification and discrimination. Second, the two-stage patch-based strategy contains two independent patch-segmentations for the face image. In the first stage, according to the Multi-value strategy we obtain the discriminative local descriptor based on small patches. In the second stage, we calculate weights for larger patches, and the discriminative face regions, such as eyes and month, are strengthened by the weights. The Two-stage strategy considers local similarity in the first stage and global differences in the second one. Extensive experiments on Extended Yale B and FERET show that our method outperforms state-of-the-art methods.