2DPCA versus PCA for face recognition  

作　　者：胡建军 谭冠政 栾凤刚 A.S.M.LIBDA 

机构地区：[1]School of Information Science and Engineering, Central South University [2]School of National Defense Engineering, PLA University of Science and Technology

出　　处：《Journal of Central South University》2015年第5期1809-1816,共8页中南大学学报（英文版）

基　　金：Projects(50275150,61173052)supported by the National Natural Science Foundation of China

摘　　要：Dimensionality reduction methods play an important role in face recognition. Principal component analysis(PCA) and two-dimensional principal component analysis(2DPCA) are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference(CID), was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.Dimensionality reduction methods play an important role in face recognition. Principal component analysis（PCA） and two-dimensional principal component analysis（2DPCA） are two kinds of important methods in this field. Recent research seems like that 2DPCA method is superior to PCA method. To prove if this conclusion is always true, a comprehensive comparison study between PCA and 2DPCA methods was carried out. A novel concept, called column-image difference（CID）, was proposed to analyze the difference between PCA and 2DPCA methods in theory. It is found that there exist some restrictive conditions when2 DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.

关 键 词：face recognition dimensionality reduction 2DPCA method PCA method column-image difference(CID) 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TQ245.12[自动化与计算机技术—计算机科学与技术]