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Combining Spatial and Temporal Information for Gait Based Gender Classification
Maodi Hu; Yunhong Wang; Zhaoxiang Zhang; Yiding Wang
Conference NameInternational Conference on Pattern Recognition
Source PublicationICPR 2010
Conference Date23-26 August 2010
Conference PlaceIstanbul, Turkey
AbstractIn 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.
KeywordSpatio-temporal Property Gait Analysis Gender Classification
Document Type会议论文
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
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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