Study for Within-Class Average Face Method Based on PCA in Face Recognition
PCA类内平均脸法在人脸识别中的应用研究

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.