Fuzzy kernel discriminant analysis (FKDA) and its application to face recognition

Author

Wu, Xiao-jun ; Gu, Li-min ; Wang, Shi-Tong ; Yang, Jing-Yu ; Zheng, Yu-jie ; Yu, Dong-jun

Author_Institution

Southern Yangtze Univ., Wuxi

Volume

1

fYear

2007

fDate

2-4 Nov. 2007

Firstpage

118

Lastpage

123

Abstract

A fuzzy kernel diccriminant analysis algorithm (FKDA) is proposed in this paper, which is the kernel version of the fuzzy fisherface method. First, KPCA is performed on the training data. Then fuzzy k-nearest neighbor (FKNN) is introduced to find the mean vectors of each class. Fuzzy scatter matrices are derived for fuzzy LDA in the kernel space. The results of experiments conducted on ORL database show that the proposed method is better than fuzzy fisherface method in terms of accurate recognition rate.

Keywords

face recognition; fuzzy set theory; ORL database; face recognition; fuzzy fisherface method; fuzzy k-nearest neighbor; fuzzy kernel discriminant analysis; fuzzy scatter matrices; Algorithm design and analysis; Face recognition; Humans; Information analysis; Kernel; Linear discriminant analysis; Pattern analysis; Pattern recognition; Scattering; Wavelet analysis; Fisherface; feature extraction; fuzzy k-nearest neighbor; kernel method;

fLanguage

English

Publisher

ieee

Conference_Titel

Wavelet Analysis and Pattern Recognition, 2007. ICWAPR '07. International Conference on

Conference_Location

Beijing

Print_ISBN

978-1-4244-1065-1

Electronic_ISBN

978-1-4244-1066-8

Type

conf

DOI

10.1109/ICWAPR.2007.4420648

Filename

4420648