CASIA OpenIR
GaitNet: An end-to-end network for gait based human identification
Song, Chunfeng1; Huang, Yongzhen1; Huang, Yan1; Jia, Ning2; Wang, Liang1
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2019-12-01
Volume96Issue:106988Pages:11
Abstract

Gait recognition is one of the most important techniques for human identification at a distance. Most current gait recognition frameworks consist of several separate steps: silhouette segmentation, feature extraction, feature learning, and similarity measurement. These modules are mutually independent with each part fixed, resulting in a suboptimal performance in challenging conditions. In this paper, we integrate those steps into one framework, i.e., an end-to-end network for gait recognition, named GaitNet. It is composed of two convolutional neural networks: one corresponds to gait segmentation, and the other corresponds to classification. The two networks are modeled in one joint learning procedure which can be trained jointly. This strategy greatly simplifies the traditional step-by-step manner and is thus much more efficient for practical applications. Moreover, joint learning can automatically adjust each part to fit the global optimal objective, leading to obvious performance improvement over separate learning. We evaluate our method on three large scale gait datasets, including CASIA-B, SZU RGB-D Gait and a newly built database with complex dynamic outdoor backgrounds. Extensive experimental results show that the proposed method is effective and achieves the state-of-the-art results. The code and data will be released upon request. (C) 2019 Elsevier Ltd. All rights reserved.

KeywordGait recognition Video-based human identification End-to-end CNN Joint learning
DOI10.1016/j.patcog.2019.106988
WOS KeywordRECOGNITION ; REPRESENTATION ; IMAGE ; MODEL ; FRAMEWORK ; QUALITY
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61525306] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61420106015] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61806194] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Beijing Science and Technology Project[Z181100008918010] ; National Key Research and Development Program of China[2016YEB1001000] ; CAS-AIR ; NVIDIA ; NVIDIA DGX-1 Al Supercomputer
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000487569700017
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26439
Collection中国科学院自动化研究所
Corresponding AuthorWang, Liang
Affiliation1.Chinese Acad Sci, Univ Chinese Acad Sci, Natl Lab Pattern Recognit, Ctr Res Intelligent Percept & Comp,Inst Automat, Beijing 100190, Peoples R China
2.Univ Durham, Durham DH1 3LE, England
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Song, Chunfeng,Huang, Yongzhen,Huang, Yan,et al. GaitNet: An end-to-end network for gait based human identification[J]. PATTERN RECOGNITION,2019,96(106988):11.
APA Song, Chunfeng,Huang, Yongzhen,Huang, Yan,Jia, Ning,&Wang, Liang.(2019).GaitNet: An end-to-end network for gait based human identification.PATTERN RECOGNITION,96(106988),11.
MLA Song, Chunfeng,et al."GaitNet: An end-to-end network for gait based human identification".PATTERN RECOGNITION 96.106988(2019):11.
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