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A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs

文献类型：期刊论文

作者Wu, Zifeng1; Huang, Yongzhen2; Wang, Liang2; Wang, Xiaogang3; Tan, Tieniu2
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2017-02-01
卷号39期号:2页码:209-226
关键词Deep Learning Cnn Human Identification Gait Cross-view
DOI10.1109/TPAMI.2016.2545669
文献子类Article
英文摘要This paper studies an approach to gait based human identification via similarity learning by deep convolutional neural networks (CNNs). With a pretty small group of labeled multi-view human walking videos, we can train deep networks to recognize the most discriminative changes of gait patterns which suggest the change of human identity. To the best of our knowledge, this is the first work based on deep CNNs for gait recognition in the literature. Here, we provide an extensive empirical evaluation in terms of various scenarios, namely, cross-view and cross-walking-condition, with different preprocessing approaches and network architectures. The method is first evaluated on the challenging CASIA-B dataset in terms of cross-view gait recognition. Experimental results show that it outperforms the previous state-of-the-art methods by a significant margin. In particular, our method shows advantages when the cross-view angle is large, i.e., no less than 36 degree. And the average recognition rate can reach 94 percent, much better than the previous best result (less than 65 percent). The method is further evaluated on the OU-ISIR gait dataset to test its generalization ability to larger data. OU-ISIR is currently the largest dataset available in the literature for gait recognition, with 4,007 subjects. On this dataset, the average accuracy of our method under identical view conditions is above 98 percent, and the one for cross-view scenarios is above 91 percent. Finally, the method also performs the best on the USF gait dataset, whose gait sequences are imaged in a real outdoor scene. These results show great potential of this method for practical applications.
WOS关键词CONVOLUTIONAL NETWORKS ; RECOGNITION ; PERFORMANCE ; BIOMETRICS ; PROJECTION ; IMAGE
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000395553400001
资助机构National Basic Research Program of China(2012CB316300) ; National Natural Science Foundation of China(61525306 ; 61420106015)
源URL[http://ir.ia.ac.cn/handle/173211/14376]  
专题自动化研究所_智能感知与计算研究中心
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
3.Chinese Univ Hong Kong, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Zifeng,Huang, Yongzhen,Wang, Liang,et al. A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2017,39(2):209-226.
APA Wu, Zifeng,Huang, Yongzhen,Wang, Liang,Wang, Xiaogang,&Tan, Tieniu.(2017).A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,39(2),209-226.
MLA Wu, Zifeng,et al."A Comprehensive Study on Cross-View Gait Based Human Identification with Deep CNNs".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 39.2(2017):209-226.

入库方式： OAI收割

来源：自动化研究所

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