Comparison of PCA and ICA in Face Recognition

Author

Luo, Bing ; Hao, Yu-Jie ; Zhang, Wei-Hua ; Liu, Zhi-Shen

Author_Institution

Univ. of Electron. Sci. & Technol. of China, Chengdu

fYear

2008

fDate

13-15 Dec. 2008

Firstpage

241

Lastpage

243

Abstract

Over the last ten years, face recognition has become a specialized applications area within the larger field of computer vision. Principal component analysis (PCA) and independent component analysis (ICA) become common method for face recognition. This paper compares Principal component analysis (PCA) to independent component analysis (ICA) in face recognition. In this paper, we used PCA derived from "eigenfaces". ICA derived from a linear representation of nongaussian data. In the paper, it shows the different between PCA and ICA.

Keywords

computer vision; face recognition; independent component analysis; principal component analysis; ICA; PCA; computer vision; face recognition; independent component analysis; linear representation; nongaussian data; principal component analysis; Application software; Chemical technology; Computer vision; Face recognition; Image databases; Independent component analysis; Pixel; Principal component analysis; Signal generators; Statistical analysis; Face recognition; ICA (independent component analysis); PCA (principle component analysis);

fLanguage

English

Publisher

ieee

Conference_Titel

Apperceiving Computing and Intelligence Analysis, 2008. ICACIA 2008. International Conference on

Conference_Location

Chengdu

Print_ISBN

978-1-4244-3427-5

Electronic_ISBN

978-1-4244-3426-8

Type

conf

DOI

10.1109/ICACIA.2008.4770014

Filename

4770014