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

Dat Tran; Xu Huang; Girija Chetty

doi:10.1109/NSS.2010.19

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

Dat Tran, Xu Huang, Girija Chetty

Information, Technology (IT) & Systems

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

10 Citations (Scopus)

27 Downloads (Pure)

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 language	English
Title of host publication	2010 Fourth International Conference on Network and System Security: NSS 2010
Editors	Yang Xiang, Pierangela Samarati, Jiankun Hu, Wanlei Zhou, Ahmad-Reza Sadeghi
Place of Publication	Piscataway, N.J., USA
Publisher	IEEE
Pages	579-582
Number of pages	4
ISBN (Print)	9781424484843
DOIs	https://doi.org/10.1109/NSS.2010.19
Publication status	Published - 2010
Event	Fourth International Conference on Network and System Security (NSS 2010), - Melbourne, Australia Duration: 1 Sep 2010 → 3 Sep 2010

Conference

Conference	Fourth International Conference on Network and System Security (NSS 2010),
Country/Territory	Australia
City	Melbourne
Period	1/09/10 → 3/09/10

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10.1109/NSS.2010.19

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@inproceedings{7daa17d5c65c44679ecc273a031a4e58,

title = "Face gender recognition based on 2D principal component analysis and support vector machine",

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.",

author = "Dat Tran and Xu Huang and Girija Chetty",

year = "2010",

doi = "10.1109/NSS.2010.19",

language = "English",

isbn = "9781424484843",

pages = "579--582",

editor = "Yang Xiang and Pierangela Samarati and Jiankun Hu and Wanlei Zhou and Ahmad-Reza Sadeghi",

booktitle = "2010 Fourth International Conference on Network and System Security: NSS 2010",

publisher = "IEEE",

note = "Fourth International Conference on Network and System Security (NSS 2010), ; Conference date: 01-09-2010 Through 03-09-2010",

}

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. IEEE, Piscataway, N.J., USA, pp. 579-582, Fourth International Conference on Network and System Security (NSS 2010), Melbourne, Australia, 1/09/10. https://doi.org/10.1109/NSS.2010.19

Face gender recognition based on 2D principal component analysis and support vector machine. / Tran, Dat; Huang, Xu; Chetty, Girija.

2010 Fourth International Conference on Network and System Security: NSS 2010. ed. / Yang Xiang; Pierangela Samarati; Jiankun Hu; Wanlei Zhou; Ahmad-Reza Sadeghi. Piscataway, N.J., USA : IEEE, 2010. p. 579-582.

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Face gender recognition based on 2D principal component analysis and support vector machine

AU - Tran, Dat

AU - Huang, Xu

AU - Chetty, Girija

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

U2 - 10.1109/NSS.2010.19

DO - 10.1109/NSS.2010.19

M3 - Conference contribution

SN - 9781424484843

SP - 579

EP - 582

BT - 2010 Fourth International Conference on Network and System Security: NSS 2010

A2 - Xiang, Yang

A2 - Samarati, Pierangela

A2 - Hu, Jiankun

A2 - Zhou, Wanlei

A2 - Sadeghi, Ahmad-Reza

PB - IEEE

CY - Piscataway, N.J., USA

T2 - Fourth International Conference on Network and System Security (NSS 2010),

Y2 - 1 September 2010 through 3 September 2010

ER -

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

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