CASIA OpenIR  > 智能感知与计算研究中心
Invariant feature extraction for gait recognition using only one uniform model
Yu, Shiqi1; Chen, Haifeng1; Wang, Qing1; Shen, Linlin1; Huang, Yongzhen2
Source PublicationNEUROCOMPUTING
2017-05-24
Volume239Pages:81-93
SubtypeArticle
AbstractGait recognition has been proved useful in human identification at a distance. But many variations such as view, clothing, carrying condition make gait recognition is still challenging in real applications. The variations make it is hard to extract invariant feature to distinguish different subjects. For view variation, one view transformation model can be employed to convert the gait feature from one view to another. Most existing models need to estimate the view angle first, and can Work for only one view pair. They can not convert multi-view data to one specific view efficiently. Other Variations also need some specific models to handle. We employed one deep model based on auto-encoder for invariant gait extraction. The model can synthesize gait feature in a progressive Way by stacked multi-layer auto-encoders. The unique advantage is that it can extract invariant gait feature using only one model, and the extracted feature is robust to view, clothing and carrying condition variation. The proposed method is evaluated on two large gait datasets, CASIA Gait Dataset B and SZU RGB-D Gait Dataset. The experimental results show that the proposed method can achieve state-of-the-art performance by only one uniform model. (C) 2017 Elsevier B.V. All rights reserved.
KeywordGait Recognition Deep Learning Invariant Feature
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.neucom.2017.02.006
WOS KeywordIDENTIFICATION ; BIOMETRICS
Indexed BySCI
Language英语
Funding OrganizationNatural Science Foundation of China(61672357 ; Science Foundation of Guangdong Province(2014A030313556) ; Science Foundation of Shenzhen(JCYJ20150324141711699 ; Strategic Priority Research Program of the CAS(XDB02070001) ; Youth Innovation Promotion Association CAS(2006121) ; 61420106015) ; JCYJ20160422144110140)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000397689300008
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15072
Collection智能感知与计算研究中心
Affiliation1.Shenzhen Univ, Coll Comp Sci & Software Engn, Comp Vis Inst, Shenzhen 518060, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yu, Shiqi,Chen, Haifeng,Wang, Qing,et al. Invariant feature extraction for gait recognition using only one uniform model[J]. NEUROCOMPUTING,2017,239:81-93.
APA Yu, Shiqi,Chen, Haifeng,Wang, Qing,Shen, Linlin,&Huang, Yongzhen.(2017).Invariant feature extraction for gait recognition using only one uniform model.NEUROCOMPUTING,239,81-93.
MLA Yu, Shiqi,et al."Invariant feature extraction for gait recognition using only one uniform model".NEUROCOMPUTING 239(2017):81-93.
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