基于二维加权主元分析的人脸识别研究

Research on Face Recognition Based on Two-Dimensional Weighted Principal Component Analysis

提出了一种基于二维加权主元分析的方法进行人脸识别。该方法考虑了人脸的不同部位所包含的识别信息量不同,对人脸的不同部位赋予不同的权重,并结合二维主元分析方法求解加权子空间,然后将人脸样本向该子空间进行投影来提取人脸特征,最后采用最近邻距离分类器进行分类。该方法在NUST603人脸图像库中进行了实验,实验结果表明了该方法的有效性。

A method based on two - dimensional weighted principal component analysis is proposed in this paper. The method takes into account the fact that the different face regions contain the different recognition information, and the different face regions are given the different weighted values. The proposed method is combined with the two - dimensional principal component analysis to calculate the weighted subspace, and then face samples are projected onto the weighted subspace to extract the face features. Finally, a nearest neighbor classifier is employed to classify the extracted features. Experimental results on the NUST603 face database show the proposed method is effective.