Fuzzy Kernel Fisher Discriminant Algorithm with Application to Face Recognition

Author

Zheng, Yujie ; Yang, Jingyu ; Wang, Weidong ; Wang, Qiong ; Yang, Jian ; Wu, Xiaojun

Author_Institution

Dept. of Comput. Sci., Nanjing Univ. of Sci. & Technol.

Volume

2

fYear

0

fDate

0-0 0

Firstpage

9669

Lastpage

9672

Abstract

In this paper, a new kernel Fisher discriminant (KFD) algorithm with fuzzy set theory is studied. KFD algorithm is effective to extract nonlinear discriminative features of input samples with kernel trick. While conventional KFD algorithm assumes the same level of relevance of each sample to the corresponding class. In this paper, a novel KFD algorithm named fuzzy kernel Fisher discriminant (FKFD) is proposed. Distribution information of samples is represented with fuzzy membership degree in this paper. Furthermore, this information is utilized to redefine the corresponding scatter matrices, which are different to the conventional KFD algorithm and effective to extract discriminative features from overlapping (outlier) samples. Experimental results on ORL face database demonstrate the effectiveness of the proposed method

Keywords

face recognition; feature extraction; fuzzy set theory; matrix algebra; face recognition; fuzzy k-nearest neighbor; fuzzy kernel Fisher discriminant algorithm; fuzzy membership; fuzzy set theory; nonlinear discriminative feature extraction; scatter matrices; Application software; Data mining; Face recognition; Feature extraction; Fuzzy set theory; Kernel; Linear discriminant analysis; Machine learning; Scattering; Spatial databases; Face Recognition; Fuzzy K-Nearest Neighbor; Fuzzy Kernel Linear Discriminant Analysis; Kernel Linear Discriminant Analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713879

Filename

1713879