Gait-based gender classification in unconstrained environments
This paper investigates the problem of gait-based gender classification in unconstrained environments. Different from existing human gait analysis and recognition methods which assume that humans walk in controlled environments, we aim to recognize human gender from uncontrolled gaits in which peopl...
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sg-ntu-dr.10356-993972019-12-06T20:06:46Z Gait-based gender classification in unconstrained environments Huang, T. S. Lu, Jiwen Wang, Gang School of Electrical and Electronic Engineering International Conference on Pattern Recognition (21st : 2012 : Tsukuba, Japan) DRNTU::Engineering::Electrical and electronic engineering This paper investigates the problem of gait-based gender classification in unconstrained environments. Different from existing human gait analysis and recognition methods which assume that humans walk in controlled environments, we aim to recognize human gender from uncontrolled gaits in which people can walk freely and the walking direction of human gaits may be time-varying in a singe video clip. Given each gait sequence collected in an uncontrolled manner, we first obtain human silhouettes using background substraction and cluster them into several groups. For each group, we compute the averaged gait image (AGI) as features. Then, we learn a distance metric under which the intraclass variations are minimized and the interclass variations are maximized, simultaneously, such that more discriminative information can be exploited for gender classification. Experimental results on our dataset demonstrate the efficacy of the proposed method. 2013-08-02T03:59:27Z 2019-12-06T20:06:46Z 2013-08-02T03:59:27Z 2019-12-06T20:06:46Z 2012 2012 Conference Paper https://hdl.handle.net/10356/99397 http://hdl.handle.net/10220/12869 en |
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DRNTU::Engineering::Electrical and electronic engineering Huang, T. S. Lu, Jiwen Wang, Gang Gait-based gender classification in unconstrained environments |
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This paper investigates the problem of gait-based gender classification in unconstrained environments. Different from existing human gait analysis and recognition methods which assume that humans walk in controlled environments, we aim to recognize human gender from uncontrolled gaits in which people can walk freely and the walking direction of human gaits may be time-varying in a singe video clip. Given each gait sequence collected in an uncontrolled manner, we first obtain human silhouettes using background substraction and cluster them into several groups. For each group, we compute the averaged gait image (AGI) as features. Then, we learn a distance metric under which the intraclass variations are minimized and the interclass variations are maximized, simultaneously, such that more discriminative information can be exploited for gender classification. Experimental results on our dataset demonstrate the efficacy of the proposed method. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Huang, T. S. Lu, Jiwen Wang, Gang |
format |
Conference or Workshop Item |
author |
Huang, T. S. Lu, Jiwen Wang, Gang |
author_sort |
Huang, T. S. |
title |
Gait-based gender classification in unconstrained environments |
title_short |
Gait-based gender classification in unconstrained environments |
title_full |
Gait-based gender classification in unconstrained environments |
title_fullStr |
Gait-based gender classification in unconstrained environments |
title_full_unstemmed |
Gait-based gender classification in unconstrained environments |
title_sort |
gait-based gender classification in unconstrained environments |
publishDate |
2013 |
url |
https://hdl.handle.net/10356/99397 http://hdl.handle.net/10220/12869 |
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1681034849383612416 |