Learning view invariant gait features with Two-Stream GAN
文献类型：期刊论文
| 作者 | Wang, Yanyun1,2; Song, Chunfeng2,3 ; Huang, Yan2,3,4; Wang, Zhenyu1; Wang, Liang2,3,4
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2019-04-28 |
| 卷号 | 339页码:245-254 |
| 关键词 | Gait recognition Cross-veiw Two-Stream GAN |
| ISSN号 | 0925-2312 |
| DOI | 10.1016/j.neucom.2019.02.025 |
| 通讯作者 | Wang, Zhenyu(zywang@ncepu.edu.cn) |
| 英文摘要 | Gait recognition is an important yet challenging problem in computer vision. The changing view of gait is one of the most challenging factors, which could greatly affect the accuracy of cross-view gait recognition. In this paper, we propose a Two-Stream Generative Adversarial Network (TS-GAN) for cross-view gait recognition. For any view of gait representations, GAN can restore it to the corresponding standard view, to learn view invariant gait features. To achieve this goal, TS-GAN has two streams : (1) the global-stream can learn global contexts, and (2) the part-stream can learn local details. We combine the two streams to learn final identities. Moreover, we add a pixel-wise loss along with the generators of GAN to restore the gait details in pixel-level. We evaluate the proposed method on two widely used gait databases: CASIA-B and OU-ISIR. Experiment results show that our approach outperforms the compared state-of-the-art approaches. (C) 2019 Elsevier B.V. All rights reserved. |
| WOS关键词 | RECOGNITION ; REPRESENTATION ; IMAGE |
| 资助项目 | National Key Research and Development Program of China[2016YFB10010 0 0] ; National National Science Foundation of China[61573139] ; National National Science Foundation of China[61525306] ; National National Science Foundation of China[61633021] ; National National Science Foundation of China[61721004] ; National National Science Foundation of China[61420106015] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; Fundamental Research Funds for the Central Universities[2018ZD05] ; Beijing Science and Technology Project[Z181100008918010] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000461166500024 |
| 出版者 | ELSEVIER SCIENCE BV |
| 资助机构 | National Key Research and Development Program of China ; National National Science Foundation of China ; Capital Science and Technology Leading Talent Training Project ; Fundamental Research Funds for the Central Universities ; Beijing Science and Technology Project |
| 源URL | [http://ir.ia.ac.cn/handle/173211/25004]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Wang, Zhenyu |
| 作者单位 | 1.North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China 2.Chinese Acad Sci CASIA, CRIPAC, NLPR, Beijing 100190, Peoples R China 3.UCAS, Beijing 100190, Peoples R China 4.CEBSIT, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Yanyun,Song, Chunfeng,Huang, Yan,et al. Learning view invariant gait features with Two-Stream GAN[J]. NEUROCOMPUTING,2019,339:245-254. |
| APA | Wang, Yanyun,Song, Chunfeng,Huang, Yan,Wang, Zhenyu,&Wang, Liang.(2019).Learning view invariant gait features with Two-Stream GAN.NEUROCOMPUTING,339,245-254. |
| MLA | Wang, Yanyun,et al."Learning view invariant gait features with Two-Stream GAN".NEUROCOMPUTING 339(2019):245-254. |
入库方式： OAI收割
来源：自动化研究所
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