EMD Based Face Gender Discrimination

Author

Nie, Xiangfei ; Guo, Jun ; Yang, Zhen ; Li, Chunguang ; Wang, Jian ; Deng, Weihong

Author_Institution

PRIS Lab., Beijing Univ. of Posts & Telecommun.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

4078

Lastpage

4081

Abstract

A novel method for face gender discrimination was proposed. The method got 27 intrinsic mode functions (IMFs) by calculating empirical mode decomposition (EMD) for 3 mean faces. For face gender feature extraction, the method used these IMFs as projection vectors. Finally, kernel Fisher discriminant analysis (KFDA) and support vector machine (SVM) were used for classification, respectively. With the same performance for face gender discrimination, computational results show that the efficiency of EMD+KFDA method is more than 3.7 times as that of direct KFDA method, and the EMD+SVM method is at least 1.5 times faster than the PCA + SVM method

Keywords

face recognition; feature extraction; support vector machines; EMD based face gender discrimination; empirical mode decomposition; face gender feature extraction; intrinsic mode functions; kernel Fisher discriminant analysis; projection vectors; support vector machine; Automation; Feature extraction; Intelligent control; Kernel; Linear discriminant analysis; Principal component analysis; Support vector machine classification; Support vector machines; empirical mode decomposition (EMD); face gender discrimination; intrinsic mode functions (IMFs); kernel Fisher discriminant analysis (KFDA); support vector machine(SVM);

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.2006.1713141

Filename

1713141