关键词: 人脸识别, 光照变化, 高斯差分变换, SRC分类器, 低错误接受率

Abstract: The aim of this paper is to solve the problem of large variation in illumination in face recognition.The Difference Of Gaussian（DOG） filters are adopted in the preprocessing step to correct face images in the original dataset under extremes of illumination conditions.The resulting image dataset is combined with the original dataset to provide more sufficient information to maintain system performance for normally illuminated images.Sparse Representation Classification（SRC） instead of traditional classification is introduced to improve the recognition rate at a low false acceptance rate and solve the problem that faces can not be recognized because of the variations in lighting.Experimental results on the Yale-B face database，CMU-PIE face database and ORL face database show that the proposed method improves the recognition rate under different illumination.

Key words: face recognition, varying lighting, difference of Gaussian, Sparse Representation Classification, low false acceptance