Invariant feature extraction for gait recognition using only one uniform model | |
Yu, Shiqi1; Chen, Haifeng1; Wang, Qing1; Shen, Linlin1; 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 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/15072 |
专题 | 模式识别实验室 |
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. |
条目包含的文件 | 条目无相关文件。 |
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