Title: Human gender classification: a review

 

Author: Feng Lin; Yingxiao Wu; Yan Zhuang; Xi Long; Wenyao Xu

 

Addresses:
Department of Computer Science and Engineering, University at Buffalo (SUNY), Buffalo, NY, USA
Department of Computer Science and Engineering, University at Buffalo (SUNY), Buffalo, NY, USA
Department of Computer Science and Engineering, University at Buffalo (SUNY), Buffalo, NY, USA
Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
Department of Computer Science and Engineering, University at Buffalo (SUNY) Buffalo, NY, USA

 

Journal: Int. J. of Biometrics, 2016 Vol.8, No.3/4, pp.275 - 300

 

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 highlight 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 features; biometrics; biosignals; biological information; social network information; gender recognition; human bodies; human behaviours.

 

DOI: http://dx.doi.org/10.1504/IJBM.2016.10003589

 

Available online 27 Feb 2017

 

 

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