The Application of Fuzzy Two-Dimensional Principal Component Analysis (F2DPCA) on Face Recognition

Author

Xiao, Shaozhang ; Gao, Shangbing

Author_Institution

Fac. of Comput. Eng., Huaiyin Inst. of Technol., Huaian, China

Volume

2

fYear

2011

fDate

24-25 Sept. 2011

Firstpage

349

Lastpage

353

Abstract

This paper proposes a novel method, called fuzzy two-dimensional principal component analysis (F2DPCA), which combines the two-dimensional principal component analysis (2DPCA) and fuzzy set theory. 2DPCA preserve the total variance by maximizing the trace of feature variance, but 2DPCA cannot preserve local information due to pursuing maximal variance. So, the fuzzy two-dimensional principal component analysis (F2DPCA) algorithm is proposed, in which the fuzzy k-nearest neighbor (FKNN) is implemented to achieve the distribution local information of original samples. Experimental results on ORL and Yale face databases show the effectiveness of the proposed method.

Keywords

face recognition; feature extraction; fuzzy set theory; pattern clustering; principal component analysis; visual databases; 2DPCA; F2DPCA; ORL; Yale face database; distribution local information; face recognition; feature variance; fuzzy k-nearest neighbor; fuzzy set theory; fuzzy two-dimensional principal component analysis; maximal variance; Covariance matrix; Databases; Face; Feature extraction; Principal component analysis; Training; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Technology, Computer Engineering and Management Sciences (ICM), 2011 International Conference on

Conference_Location

Nanjing, Jiangsu

Print_ISBN

978-1-4577-1419-1

Type

conf

DOI

10.1109/ICM.2011.67

Filename

6113539