Robust low-resolution face recognition via low-rank representation and locality-constrained regression

Recognizing face images in low-resolution (LR) scenarios have bigger challenges than recognizing those in high-resolution (HR) scenarios due to that LR images usually lack discriminative details. Previous methods ignore the existence of occlusions in the LR probe images. To alleviate this problem, we propose a low-rank representation and locality-constrained regression (LLRLCR) based method in this paper to learn occlusion-robust representations features for final face recognition tasks. For HR gallery set, LLRLCR uses double low-rank representation to reveal the underlying holistic data structures; for LR probe, LLRLCR uses locality-constrained matrix regression to keep regression error's structural information and to learn robust and discriminative representation features. The proposed method allows us to fully exploit the structure information in gallery and probe data simultaneously. Finally, after getting the occlusion-robust features, the face labels can be predicted via a simple yet powerful sparse representation based classifier engine. Experiments on some standard face databases have indicated that the proposed method can obtain promising recognition performance than some state-of-the-art LR face recognition approaches.