关键词: 人脸识别, 全局特征, 局部特征, 特征融合, 贝叶斯分类器

Abstract: Aiming at the problem that data dimensionality reduction methods are often difficult to balance the global and local feature information in the feature extraction stage of face recognition, and Bayesian classifier exists the small sample problem in the matching recognition stage, a Bayesian face recognition method based on global and local feature fusion is proposed. In this method, the global non-linear feature of face data is extracted by kernel principal component analysis, and on this basis, the local manifold structure information of face data is extracted by the orthogonal locality sensitive discriminant analysis, achieving the purpose of extracting the low-dimension essential facial feature with high-discrimination. Then, a maximum entropy covariance selection method is utilized to estimate the covariance matrix to solve the small sample problem in Bayesian classifier design. Experiments are designed on ORL, AR, YALE and FLW face database. The results show that the proposed feature extraction algorithm and the improvement of Bayesian classifier have achieved good results, and the optimization of these two stages can improve the effect of face recognition significantly.

Key words: face recognition, global feature, local feature, feature fusion, Bayesian classifier