CASIA OpenIR  > 类脑智能研究中心
Gait-Based Gender Classification Using Mixed Conditional Random Field
Maodi Hu; Yunhong Wang; Zhaoxiang Zhang; De Zhang
Source PublicationIEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics,
2011-05-27
Volume41Issue:5Pages:1429-1439
AbstractThis paper proposes a supervised modeling approach for gait-based gender classification. Different from traditional temporal modeling methods, male and female gait traits are competitively learned by the addition of gender labels. Shape appearance and temporal dynamics of both genders are integrated into a sequential model called mixed conditional random field (CRF) (MCRF), which provides an open framework applicable to various spatiotemporal features. In this paper, for the spatial part, pyramids of fitting coefficients are used to generate the gait shape descriptors; for the temporal part, neighborhood-preserving embeddings are clustered to allocate the stance indexes over gait cycles. During these processes, we employ evaluation functions like the partition index and Xie and Beni's index to improve the feature sparseness. By fusion of shape descriptors and stance indexes, the MCRF is constructed in coordination with intra- and intergender temporary Markov properties. Analogous to the maximum likelihood decision used in hidden Markov models (HMMs), several classification strategies on the MCRF are discussed. We use CASIA (Data set B) and IRIP Gait Databases for the experiments. The results show the superior performance of the MCRF over HMMs and separately trained CRFs.
KeywordStance Index Gait Analysis Gender Classification Human Motion Markov Property Mixed Conditional Random Field (Crf) (mCrf) Shape Descriptor
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13215
Collection类脑智能研究中心
Corresponding AuthorZhaoxiang Zhang
Recommended Citation
GB/T 7714
Maodi Hu,Yunhong Wang,Zhaoxiang Zhang,et al. Gait-Based Gender Classification Using Mixed Conditional Random Field[J]. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics,,2011,41(5):1429-1439.
APA Maodi Hu,Yunhong Wang,Zhaoxiang Zhang,&De Zhang.(2011).Gait-Based Gender Classification Using Mixed Conditional Random Field.IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics,,41(5),1429-1439.
MLA Maodi Hu,et al."Gait-Based Gender Classification Using Mixed Conditional Random Field".IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 41.5(2011):1429-1439.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Maodi Hu]'s Articles
[Yunhong Wang]'s Articles
[Zhaoxiang Zhang]'s Articles
Baidu academic
Similar articles in Baidu academic
[Maodi Hu]'s Articles
[Yunhong Wang]'s Articles
[Zhaoxiang Zhang]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Maodi Hu]'s Articles
[Yunhong Wang]'s Articles
[Zhaoxiang Zhang]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.