Title: Human gender classification: a review

Authors: 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

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: 10.1504/IJBM.2016.082604

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

Received: 18 Mar 2016
Accepted: 24 Oct 2016
Published online: 02 Mar 2017 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article