Shadow compensation based on facial symmetry and image average for robust face recognition

In this paper, we propose a novel shadow compensation approach for face recognition under varying illumination. This approach based on facial symmetry and image average can solve the drawbacks of the conventional algorithms such as histogram equalization (HE) and region-based histogram equalization (RHE). Among others, the proposed approach has the following advantages: (1) it is very simple, so it is easily implemented in a real-time face recognition system; (2) it is able to reinforce key facial features and to standardize other parts of the face; and (3) it can apply directly to single face image without any prior information of light source direction. The experimental results reveal that the proposed approach has a rather excellent recognition performance.