DocumentCode :
2709643
Title :
Gender Classification with Support Vector Machines Based on Non-tensor Pre-wavelets
Author :
Ying, Li ; Yu, Zhang ; Shishun, Zhao
Author_Institution :
Symbol Comput. & Knowledge Eng. Lab. of Minist. of Educ., Jilin Univ., Changchun, China
fYear :
2010
fDate :
7-10 May 2010
Firstpage :
770
Lastpage :
774
Abstract :
In this paper, a novel method for gender classifications with support vector machines based on our constructed bivariate compactly supported non-tensor product pre-wavelets is proposed. Utilizing the non-tensor product pre-wavelets to extract the more excellent gender classification features, then these features are fed into support vector machines to automatically perform gender classification. The combination of the non-tensor product pre-wavelets and SVMs for gender classification is demonstrated to be efficient by concrete numerical experiments.
Keywords :
face recognition; support vector machines; wavelet transforms; gender classification; nontensor prewavelets; support vector machines; Face recognition; Humans; Image processing; Mathematics; Nonlinear filters; Support vector machine classification; Support vector machines; Surveillance; Tensile stress; Wavelet transforms; gender classifications; pre-wavelets; support vector machines;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Research and Development, 2010 Second International Conference on
Conference_Location :
Kuala Lumpur
Print_ISBN :
978-0-7695-4043-6
Type :
conf
DOI :
10.1109/ICCRD.2010.170
Filename :
5489499
Link To Document :
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