关键词: 人脸识别, Gabor特征, 局部二值模式（LBP）特征, 核典型相关分析

Abstract: In order to improve the recognition rate of face image, a novel face recognition method （KCCA-MF） is proposed based on hybrid features fusion by kernel canonical correlation analysis. Gabor and LBP features of face images are extracted, and then the kernel correlation analysis algorithm is used to fuse two kinds of features and eliminate redundant features, the combination of face image classifier is established based on K nearest neighbor and support vector machine, and the simulation analysis is carried on three classic face databases. The results show that, compared with other face recognition methods, the proposed method improves the recognition accuracy, and can meet the real-time requirements of face recognition.

Key words: face recognition, Gabor features, Local Binary Pattern（LBP） features, kernel canonical correlation analysis