DocumentCode :
2788282
Title :
Gender classification based on fuzzy SVM
Author :
Leng, Xue-ming ; Wang, Yi-Ding
Author_Institution :
Grad. Univ. of Chinese Acad. of Sci., Beijing
Volume :
3
fYear :
2008
fDate :
12-15 July 2008
Firstpage :
1260
Lastpage :
1264
Abstract :
Generalization ability is an important issue in gender classification. In this paper a gender classifier based on Fuzzy SVM (FSVM) is developed to improve the generalization ability. The fuzzy membership used in FSVM indicates the relativity of one personpsilas face with female/male faces set. This paper proposes a novel method of generating fuzzy membership function automatically based on Learning Vector Quantization (LVQ) learning process. The method doesnpsilat rely on the apriori information of data and has strong robustness to variations such as illumination, expression and so on. The gender classifier based on FSVM is evaluated on the FERET, CAS-PEAL, BUAA-IRIP face databases. The results show that the gender classifier presented in this paper can tolerate more variations and show good performance in generalization ability.
Keywords :
face recognition; fuzzy set theory; generalisation (artificial intelligence); image classification; support vector machines; vector quantisation; fuzzy SVM; fuzzy membership function; gender classification; gender classifier; generalization ability; learning vector quantization learning process; Cybernetics; Image databases; Independent component analysis; Machine learning; Neural networks; Robustness; Support vector machine classification; Support vector machines; Testing; Vector quantization; Adaboost; FSVM; Gabor wavelet; Gender classification; Generalization ability; LVQ; Membership;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
Type :
conf
DOI :
10.1109/ICMLC.2008.4620598
Filename :
4620598
Link To Document :
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