Improved Face Recognition Method Using Genetic Principal Component Analysis  

作　　者：E.Gomathi K.Baskaran 

机构地区：[1]Department of Electronic and Communication Engineering,Karpagam Engineering College [2]Department of Computer Science and Engineering,Government College of Technology

出　　处：《Journal of Electronic Science and Technology》2010年第4期372-378,共7页电子科技学刊（英文版）

摘　　要：An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL （Olivetti Research Labs） face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods.An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL （Olivetti Research Labs） face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods.

关 键 词：EIGENFACES EIGENVECTORS face recognition genetic algorithm principal component analysis. 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TU318[自动化与计算机技术—计算机科学与技术]