PCA类内平均脸法在人脸识别中的应用研究*

人脸识别是生物特征识别技术中一个非常活跃的课题，取得了很多研究成果。统计主元分析法(Principal Components Analysis, PCA)是人脸特征提取和识别的常用方法之一。结合传统PCA算法的特点，提出了一种用类内平均脸对类内样本进行规范化的方法。该方法有效地增加了类间样本的识别距离、有效地缩小了类内样本的识别距离，从而提高了人脸正确识别率。基于ORL人脸数据库的实验结果表明，该方法正确识别率达到98%，在人脸识别的实际应用中是一种可行的方法。

Study for Within Class Average Face Method Based on PCA in Face Recognition

Face recognition is an active subject in the area of biometrical recognition technology,and lots of achievements have been obtained.Principal Components Analysis(PCA) is a basic method widely used in face feature extraction and recognition.In this paper,combined with the characteristics of traditional PCA,a method based on normalization of within-class average face image is presented,in which the classification distance of between-class samples is enlarged, while the classification distance of within-class samples is reduced.Thus face correct recognition rate is improved.Experimental results on ORL face database show that the method discussed has reached 98% of correct recognition rate,and is feasible in practical applications of face recognition.