Face gender recognition based on 2D principal component analysis and support vector machine

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Abstract

This paper presents a novel method for solving face gender recognition problem. This method employs 2D Principal Component Analysis, one of the prominent methods for extracting feature vectors, and Support Vector Machine, the most powerful discriminative method for classification. Experiments for the proposed approach have been conducted on FERET data set and the results show that the proposed method could improve the classification rates.
Original languageEnglish
Title of host publication2010 Fourth International Conference on Network and System Security: NSS 2010
EditorsYang Xiang, Pierangela Samarati, Jiankun Hu, Wanlei Zhou, Ahmad-Reza Sadeghi
Place of PublicationPiscataway, N.J., USA
PublisherIEEE
Pages579-582
Number of pages4
ISBN (Print)9781424484843
DOIs
Publication statusPublished - 2010
EventFourth International Conference on Network and System Security (NSS 2010), - Melbourne, Australia
Duration: 1 Sep 20103 Sep 2010

Conference

ConferenceFourth International Conference on Network and System Security (NSS 2010),
CountryAustralia
CityMelbourne
Period1/09/103/09/10

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Cite this

Tran, D., Huang, X., & Chetty, G. (2010). Face gender recognition based on 2D principal component analysis and support vector machine. In Y. Xiang, P. Samarati, J. Hu, W. Zhou, & A-R. Sadeghi (Eds.), 2010 Fourth International Conference on Network and System Security: NSS 2010 (pp. 579-582). Piscataway, N.J., USA: IEEE. https://doi.org/10.1109/NSS.2010.19