关键词: 特征融合, 复杂光照, 特征提取, 人脸识别

Abstract: In order to improve the accuracy of face recognition under complex illumination, a face recognition algorithm based on improved single scale Retinex and Local Binary Pattern（LBP） is proposed. Firstly, bilateral filtering is used to replace Gaussian filtering in the Retinex to process face images. At the same time, Laplacian of Gaussian（LoG） and normalization are applied to extract the edge details features of face images. The two processed images are fused by the weighting method of standard deviation. Then, LBP is used to extract features from fused images. Finally, Sparse Representation based Classification algorithm（SRC） is used to classify. Experiments on AR and Yale B+ face database show that the illumination robustness of face recognition is improved under complex illumination. It can achieve good recognition results under less training samples and complex illumination.

Key words: feature fusion, complex illumination, feature extraction, face recognition