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The Scientific World Journal
Volume 2014, Article ID 513240, 12 pages
Research Article

Gender Recognition from Unconstrained and Articulated Human Body

1Department of Computer Science, Jiangnan University, Wuxi, Jiangsu 214122, China
2Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV 26506, USA

Received 5 January 2014; Accepted 17 March 2014; Published 7 April 2014

Academic Editors: L. Lin and A. Subasi

Copyright © 2014 Qin Wu and Guodong Guo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.