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Combining Spatial and Temporal Information for Gait Based Gender Classification
Maodi Hu; Yunhong Wang; Zhaoxiang Zhang; Yiding Wang
2010-08-23
会议名称International Conference on Pattern Recognition
会议录名称ICPR 2010
会议日期23-26 August 2010
会议地点Istanbul, Turkey
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.
关键词Spatio-temporal Property Gait Analysis Gender Classification
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/13301
专题类脑智能研究中心
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],2010.
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