Face recognition by fusing binary edge feature and second-order mutual information

Illumination change and expression variation are two main factors affecting the performance of some existing face recognition algorithms. Edge feature based methods are robust to illumination change and are easy to implement. But they don’t work well for the images with expression variation. In this paper, a novel face recognition method based on the fusion of binary edge feature and grayscale information is proposed to improve both the illumination and expression robustness. The second-order mutual information (MI
2
) is introduced as the similarity metric of grayscale face images. Experimental results on AR dataset and Yale dataset, both with illumination and expression changes, show that the overall face recognition rate of the proposed method is better than that of some commonly used approaches, indicating that our method is more effective for practical uses.