Invariant feature extraction for gait recognition using only one uniform model

doi:10.1016/j.neucom.2017.02.006

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, Shiqi 1; Chen, Haifeng 1; Wang, Qing 1; Shen, Linlin 1; Huang, Yongzhen2
发表期刊	NEUROCOMPUTING
	2017-05-24
卷号	239 页码:81-93
文章类型	Article
摘要	Gait 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.
关键词	Gait Recognition Deep Learning Invariant Feature
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.neucom.2017.02.006
关键词[WOS]	IDENTIFICATION ; BIOMETRICS
收录类别	SCI
语种	英语
项目资助者	Natural 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研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000397689300008
引用统计	被引频次：129[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/15072
专题	智能感知与计算研究中心
作者单位	1.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
推荐引用方式 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.