Occluded face recognition using low-rank regression with generalized gradient direction

In this paper, we propose a the gradient direction-based hierarchical adaptive sparse and low-rank (GDHASLR) model, to solve the real-world occluded face recognition problem. In the real-world scenario, neutral face images as training data are very few, usually a single image per subject. The proposed GDHASLR has the ability to tackle this scenario. We first utilize the robustness of image gradient direction features with the proposed generalized image gradient direction. We then propose a novel hierarchical sparse and low-rank model, which combines sparse representation on dictionary learning and low-rank representation on the error, which are usually messy in the gradient direction domain. We call this scenario the weak low-rankness optimization. We solve this problem efficiently under the alternating direction method of multipliers framework, resulting in the optimum error term that has a similar weak low rank structure as the reference error map. The recognition accuracy can be enhanced greatly via weak low-rankness optimization. Extensive experiments are conducted using real-world disguise/occlusion data and synthesized contiguous occlusion data. These results show that with very few neutral face images as training data, the proposed GD-HASLR model has the best performance compared to other state-of-theart methods, including popular convoluntional neural nework-based methods. (C) 2018 Elsevier Ltd. All rights reserved.