Abstract: The face recognition is an active subject in the field of computer vision and pattern recognition，which has a wide range of potential applications.In this paper，the Fisherfaces method based on the fusion of global and local facial features is presented.Fisher linear discriminating analysis is performed to extract the most discriminating features.Both local and global features’ excellence is fused in the method.The experiments on the ORL database demonstrate the effectiveness and feasibility of the presented method.

Key words: face recognition, principle component analysis, local features, global features, Fisher linear rule, the most discriminating features

摘要： 提出了一种融合全局和局部特征的Fisherfaces方法。在Fisher线性准则下，抽取出图像全局特征和局部特征的最佳分类特征。计算待识别样本和训练样本集的加权欧氏距离。在最近邻准则下，判别待识别样本的类别，在ORL人脸库上进行的对比实验结果表明该方法的优越性。

关键词: 人脸识别, 主成分分析, 全局特征, 局部特征, Fisher线性准则, 最佳分类特征