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. ...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.展开更多
基金Projects(50275150,61173052)supported by the National Natural Science Foundation of China
文摘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.
