Adaptive illumination normalization via adaptive illumination preprocessing and modified weber-face

Abstract

Illumination processing is a challenging task in face recognition. This paper proposes a novel illumination normalization method that aims to remove illumination boundaries and improve image quality under dark conditions. Firstly, to improve the image quality, an adaptive illumination preprocessing algorithm is adopted. Then we modify the Weber-Face model by suppressing the components which are greatly affected by the illumination. Experimental results on both Extended Yale B and CMU-PIE databases show that the proposed method can obtain high performance under complex illumination conditions. The accuracy on the Extended Yale B database is 93.02% and on the CMU-PIE database is 70.44%, which is the highest among the similar approaches. This method not only greatly improves the face recognition rate but also keep the computational complexity in low compared with several state-of-the-art methods.