2DPCA versus PCA for face recognition
来源期刊：中南大学学报(英文版)2015年第5期
论文作者：HU Jian-jun TAN Guan-zheng LUAN Feng-gang A. S. M. LIBDA
文章页码：1809 - 1816
Key words：face recognition; dimensionality reduction; 2DPCA method; PCA method; column-image difference (CID)
Abstract: 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 when 2DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.
HU Jian-jun(胡建军)1, TAN Guan-zheng(谭冠政)1, LUAN Feng-gang(栾凤刚)2, A. S. M. LIBDA1
(1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. School of National Defense Engineering, PLA University of Science and Technology, Nanjing 210007, China)
Abstract: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 when 2DPCA outperforms PCA. After theoretical analysis, the experiments were conducted on four famous face image databases. The experiment results confirm the validity of theoretical claim.
Key words:face recognition; dimensionality reduction; 2DPCA method; PCA method; column-image difference (CID)
