Face recognition under varying lighting conditions: improving the recognition accuracy for local descriptors based on weber-face followed by difference of Gaussians

Illumination variation is among the several bottlenecks in a face recognition system because it can greatly affect the appearance of a face image, which causes a reduction in the face matching performance of the system. Using illumination preprocessing methods is an effective approach to overcome this problem. Despite the achievements made, however, each method still has its own demerits. In this paper, an efficient representation method insensitive to varying illumination based on a combination of Weber-face (WF) and the difference of Gaussians (DoG) methods is proposed for human face recognition using local descriptors. After processing by our method, the obtained image will preserve more facial features and edge information while shading effects are eliminated. To demonstrate the potential of the proposed method, two systems using nearest-neighbor and support vector machine classifiers were implemented. Experimental results for the CMU-PIE and extended Yale B face databases showed that our method could effectively eliminate the effect of uneven illumination and achieved better recognition accuracies in comparison to other state-of-the-art methods (DoG, gradient face, histogram equalization, and WF).