Occluded face recognition using low-rank regression with generalized gradient direction
Journal
Pattern Recognition
Journal Volume
80
Pages
256-268
Date Issued
2018
Author(s)
Wu, C.Y.
Abstract
In this paper, we propose a the gradient direction-based hierarchical adaptive sparse and low-rank (GD-HASLR) 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 GD-HASLR 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-the-art methods, including popular convoluntional neural nework-based methods.
Type
journal article