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GaitGANv2: Invariant gait feature extraction using generative adversarial networks

文献类型：期刊论文

作者Yu, Shiqi1; Liao, Rijun1; An, Weizhi1; Chen, Haifeng1; Garcia, Edel B.2; Huang, Yongzhen3,4; Poh, Norman5
刊名PATTERN RECOGNITION
出版日期2019-03-01
卷号87页码:179-189
关键词Gait recognition Generative adversarial networks Invariant feature
ISSN号0031-3203
DOI10.1016/j.patcog.2018.10.019
通讯作者Yu, Shiqi(shiqi.yu@szu.edu.cn)
英文摘要The performance of gait recognition can be adversely affected by many sources of variation such as view angle, clothing, presence of and type of bag, posture, and occlusion, among others. To extract invariant gait features, we proposed a method called GaitGANv2 which is based on generative adversarial networks (GAN). In the proposed method, a GAN model is taken as a regressor to generate a canonical side view of a walking gait in normal clothing without carrying any bag. A unique advantage of this approach is that, unlike other methods, GaitGANv2 does not need to determine the view angle before generating invariant gait images. Indeed, only one model is needed to account for all possible sources of variation such as with or without carrying accessories and varying degrees of view angle. The most important computational challenge, however, is to address how to retain useful identity information when generating the invariant gait images. To this end, our approach differs from the traditional GAN in that GaitGANv2 contains two discriminators instead of one. They are respectively called fake/real discriminator and identification discriminator. While the first discriminator ensures that the generated gait images are realistic, the second one maintains the human identity information. The proposed GaitGANv2 represents an improvement over GaitGANvl in that the former adopts a multi-loss strategy to optimize the network to increase the inter-class distance and to reduce the intra-class distance, at the same time. Experimental results show that GaitGANv2 can achieve state-of-the-art performance. (C) 2018 Elsevier Ltd. All rights reserved.
WOS关键词RECOGNITION ; PROJECTION
资助项目Science Foundation of Shenzhen[JCYJ20150324141711699] ; Science Foundation of Shenzhen[20170504160426188]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000453338200015
出版者ELSEVIER SCI LTD
资助机构Science Foundation of Shenzhen
源URL[http://ir.ia.ac.cn/handle/173211/25661]  
专题自动化研究所_智能感知与计算研究中心
通讯作者Yu, Shiqi
作者单位1.Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen, Peoples R China
2.Adv Technol Applicat Ctr CENATAV, Havana, Cuba
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
4.Watrix Technol Ltd Co Ltd, Suzhou, Peoples R China
5.Trust Stamp, Atlanta, GA USA
推荐引用方式
GB/T 7714		 Yu, Shiqi,Liao, Rijun,An, Weizhi,et al. GaitGANv2: Invariant gait feature extraction using generative adversarial networks[J]. PATTERN RECOGNITION,2019,87:179-189.
APA Yu, Shiqi.,Liao, Rijun.,An, Weizhi.,Chen, Haifeng.,Garcia, Edel B..,...&Poh, Norman.(2019).GaitGANv2: Invariant gait feature extraction using generative adversarial networks.PATTERN RECOGNITION,87,179-189.
MLA Yu, Shiqi,et al."GaitGANv2: Invariant gait feature extraction using generative adversarial networks".PATTERN RECOGNITION 87(2019):179-189.

入库方式： OAI收割

来源：自动化研究所

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