An efficient illumination compensation scheme for face recognition
This paper proposes a novel illumination compensation algorithm, which can compensate for the uneven illuminations on human faces and reconstruct face images of normal lighting conditions. A simple yet effective local contrast enhancement method, namely block-based histogram equalization (BHE), is first proposed. The resulting image processed by BHE is then compared with the original face image processed using histogram equalization (HE) to estimate the lighting category. Based on the category identified, a corresponding lighting compensation model is used to reconstruct an image that would visually be under normal illumination. In order to eliminate the influence of uneven illumination while retaining the shape information, a 2D face shape model is used. Experimental results show that, with the use of principal component analysis (PCA) for face recognition, the recognition rate can be improved by 53.3% to 56,1% when our proposed algorithm for lighting compensation is used.