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 | 模式识别实验室 |
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. |
条目包含的文件 | 条目无相关文件。 |
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