GaitGANv2: Invariant gait feature extraction using generative adversarial networks

doi:10.1016/j.patcog.2018.10.019

CASIA OpenIR > 模式识别实验室

	GaitGANv2: Invariant gait feature extraction using generative adversarial networks
	Yu, Shiqi 1; Liao, Rijun 1; An, Weizhi 1; Chen, Haifeng 1; Garcia, Edel B.2; Huang, Yongzhen3,4 ; Poh, Norman 5
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
	2019-03-01
卷号	87 页码:179-189
通讯作者	Yu, Shiqi(shiqi.yu@szu.edu.cn)
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.
关键词	Gait recognition Generative adversarial networks Invariant feature
DOI	10.1016/j.patcog.2018.10.019
关键词[WOS]	RECOGNITION ; PROJECTION
收录类别	SCI
语种	英语
资助项目	Science Foundation of Shenzhen[JCYJ20150324141711699] ; Science Foundation of Shenzhen[20170504160426188] ; Science Foundation of Shenzhen[JCYJ20150324141711699] ; Science Foundation of Shenzhen[20170504160426188]
项目资助者	Science Foundation of Shenzhen
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000453338200015
出版者	ELSEVIER SCI LTD
引用统计	被引频次：51[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/25662
专题	模式识别实验室
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.