Generalized 2D principal component analysis

Author

Kong, Hui ; Li, Xuchun ; Wang, Lei ; Teoh, Eam Khwang ; Wang, Jian-Gang ; Venkateswarlu, Ronda

Author_Institution

Sch. of Electr. & Electron. Enineering, Nanyang Technol. Univ., Singapore

Volume

1

fYear

2005

fDate

31 July-4 Aug. 2005

Firstpage

108

Abstract

A two-dimensional principal component analysis (2DPCA) by J. Yang et al. (2004) was proposed and the authors have demonstrated its superiority over the conventional principal component analysis (PCA) in face recognition. But the theoretical proof why 2DPCA is better than PCA has not been given until now. In this paper, the essence of 2DPCA is analyzed and a framework of generalized 2D principal component analysis (G2DPCA) is proposed to extend the original 2DPCA in two perspectives: a bilateral-projection-based 2DPCA (B2DPCA) and a kernel-based 2DPCA (K2DPCA) schemes are introduced. Experimental results in face recognition show its excellent performance.

Keywords

principal component analysis; bilateral-projection-based 2DPCA; face recognition; generalized 2D principal component analysis; kernel-based 2DPCA; Covariance matrix; Face recognition; Feature extraction; Kernel; Lighting; Linear discriminant analysis; Performance analysis; Principal component analysis; Signal processing; Sparse matrices;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2005. IJCNN '05. Proceedings. 2005 IEEE International Joint Conference on

Print_ISBN

0-7803-9048-2

Type

conf

DOI

10.1109/IJCNN.2005.1555814

Filename

1555814