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Human gender classification: a review

Human gender classification: a review The gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis, since it contains a wide range of information regarding the characteristics difference between male and female. Some have proposed various approaches for automatic gender classification using the features derived from human bodies and/or behaviours. First, this paper introduces the challenge and application of gender classification research. Then, the development and framework of gender classification are described. We compare these state-of-the-art approaches, including vision-based methods, biological information-based methods, and social network information-based methods, to provide a comprehensive review of gender classification research. Next we light the strength and discuss the limitation of each method. Finally, this review also discusses several promising applications for future work. Keywords: gender classification; vision-based feature; biometrics; bio-signals; social network information. Reference to this paper should be made as fols: Lin, F., Wu, Y., Zhuang, Y., Long, X. and Xu, W. (2016) `Human gender classification: a review', Int. J. Biometrics, Vol. 8, Nos. 3/4, pp.275­300. Copyright © 2016 Inderscience Enterprises Ltd. F. Lin et al. Biographical notes: Feng Lin received his BS degree from Zhejiang University, China, an MS http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Biometrics Inderscience Publishers

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Publisher
Inderscience Publishers
Copyright
Copyright © 2016 Inderscience Enterprises Ltd.
ISSN
1755-8301
eISSN
1755-831X
DOI
10.1504/IJBM.2016.082604
Publisher site
See Article on Publisher Site

Abstract

The gender recognition is essential and critical for many applications in the commercial domains such as applications of human-computer interaction and computer-aided physiological or psychological analysis, since it contains a wide range of information regarding the characteristics difference between male and female. Some have proposed various approaches for automatic gender classification using the features derived from human bodies and/or behaviours. First, this paper introduces the challenge and application of gender classification research. Then, the development and framework of gender classification are described. We compare these state-of-the-art approaches, including vision-based methods, biological information-based methods, and social network information-based methods, to provide a comprehensive review of gender classification research. Next we light the strength and discuss the limitation of each method. Finally, this review also discusses several promising applications for future work. Keywords: gender classification; vision-based feature; biometrics; bio-signals; social network information. Reference to this paper should be made as fols: Lin, F., Wu, Y., Zhuang, Y., Long, X. and Xu, W. (2016) `Human gender classification: a review', Int. J. Biometrics, Vol. 8, Nos. 3/4, pp.275­300. Copyright © 2016 Inderscience Enterprises Ltd. F. Lin et al. Biographical notes: Feng Lin received his BS degree from Zhejiang University, China, an MS

Journal

International Journal of BiometricsInderscience Publishers

Published: Jan 1, 2016

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