Combining Spatial and Temporal Information for Gait Based Gender Classification

	Combining Spatial and Temporal Information for Gait Based Gender Classification
	Maodi Hu; Yunhong Wang; Zhaoxiang Zhang; Yiding Wang
	2010-08-23
会议日期	23-26 August 2010
会议地点	Istanbul, Turkey
关键词	Spatio-temporal Property Gait Analysis Gender Classification
英文摘要	In this paper, we address the problem of gait based gender classification. The Gabor feature which is a new attempt for gait analysis, not only improves the robustness to the segmental noise, but also provides a feasible way to purge the additional influence factors like clothing and carrying condition changes before supervised learning. Furthermore, through the agency of Maximization of Mutual Information (MMI), the low dimensional discriminative representation is obtained as the Gabor-MMI feature. After that, gender related Gaussian Mixture Model-Hidden Markov Models (GMM-HMMs) are constructed for classification work. In this case, supervised learning reduces the dimension of parameter space, and significantly increases the gap between likelihoods of the gender models. In order to assess the performance of our proposed approach, we compare it with other methods on the standard CASIA Gait Databases (Dataset B). Experimental results demonstrate that our approach achieves better Correct Classification Rate (CCR) than the state of the art methods.
会议录	ICPR 2010
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/13301]
专题	自动化研究所_类脑智能研究中心
通讯作者	Zhaoxiang Zhang
推荐引用方式 GB/T 7714	Maodi Hu,Yunhong Wang,Zhaoxiang Zhang,et al. Combining Spatial and Temporal Information for Gait Based Gender Classification[C]. 见:. Istanbul, Turkey. 23-26 August 2010.