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Multi-Task GANs for View-Specific Feature Learning in Gait Recognition
He, Yiwei1,2; Zhang, Junping1,2; Shan, Hongming3; Wang, Liang4
Source PublicationIEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
2019
Volume14Issue:1Pages:102-113
SubtypeArticle
AbstractGait recognition is of great importance in the fields of surveillance and forensics to identify human beings since gait is the unique biometric feature that can be perceived efficiently at a distance. However, the accuracy of gait recognition to some extent suffers from both the variation of view angles and the deficient gait templates. On one hand, the existing cross-view methods focus on transforming gait templates among different views, which may accumulate the transformation error in a large variation of view angles. On the other hand, a commonly used gait energy image template loses temporal information of a gait sequence. To address these problems, this paper proposes multi-task generative adversarial networks (MGANs) for learning view-specific feature representations. In order to preserve more temporal information, we also propose a new multi-channel gait template, called period energy image (PEI). Based on the assumption of view angle manifold, the MGANs can leverage adversarial training to extract more discriminative features from gait sequences. Experiments on OU-ISIR, CASIA-B, and USF benchmark data sets indicate that compared with several recently published approaches, PEI + MGANs achieves competitive performance and is more interpretable to cross-view gait recognition.
KeywordGait Recognition Cross-view Generative Adversarial Networks Surveillance
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TIFS.2018.2844819
WOS KeywordHUMAN IDENTIFICATION ; PERFORMANCE
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61673118) ; Shanghai Pujiang Program(16PJD009)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000440782400002
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21841
Collection智能感知与计算研究中心
Affiliation1.Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China
2.Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China
3.Rensselaer Polytech Inst, Dept Biomed Engn, Ctr Biotechnol & Interdisciplinary Studies, Troy, NY 12180 USA
4.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
He, Yiwei,Zhang, Junping,Shan, Hongming,et al. Multi-Task GANs for View-Specific Feature Learning in Gait Recognition[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2019,14(1):102-113.
APA He, Yiwei,Zhang, Junping,Shan, Hongming,&Wang, Liang.(2019).Multi-Task GANs for View-Specific Feature Learning in Gait Recognition.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,14(1),102-113.
MLA He, Yiwei,et al."Multi-Task GANs for View-Specific Feature Learning in Gait Recognition".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 14.1(2019):102-113.
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