关键词: 人脸识别, 型相关分析, 子模式, 主成分分析

Abstract: In order to improve the recognition rate of face image, a novel face recognition method is proposed based on sub-pattern and canonical correlation analysis. The global and local features are extracted, and the redundant information between the features is eliminated, and then the face images are divided in sub models to avoid small sample, nonlinear problems, and the recognition results are corrected by voting method to increase stability of the algorithm, three face data sets are used to test the performance of the algorithm. The simulation results show that, SUB-CCA improves the recognition rate of face image compared with other algorithms.

Key words: face recognition, canonical correlation analysis, sub-pattern, Principal Component Analysis（PCA）