Sparse representation based classification method has been successfully applied to face recognition in recent years. However, it is still a problem in the scenario of pose variation in face recognition with single sample per person. In this paper, we propose a novel alignment-free model, called Gabor-based Partial Face Sparse Representation (GPFSR), to solve the problem of pose variation in face recognition with single sample per person by using partial face. In our method, we firstly locate five facial landmarks in different images. Then partial face is obtained, which is used to construct Gabor-based local dictionary and compute the weights of each patch. Our classification principle is based on sparse representation. The experimental results on the Multi-PIE and FERET show that GPFSR is robust to pose variation in FR with single sample per person.
['Jianquan Gu', 'Haifeng Hu', 'Haoxi Li', 'Weipeng Hu']
Patch-based alignment-free generic sparse representation for pose-robust face recognition