Title | FedGait: A Benchmark for Federated Gait Recognition |
Author | |
DOI | |
Publication Years | 2022
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Conference Name | 26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)
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ISSN | 1051-4651
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ISBN | 978-1-6654-9063-4
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Source Title | |
Pages | 1371-1377
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Conference Date | 21-25 Aug. 2022
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Conference Place | Montreal, QC, Canada
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Publication Place | 345 E 47TH ST, NEW YORK, NY 10017 USA
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Publisher | |
Abstract | Gait recognition has been greatly improved by deep learning and can achieve a relative high accuracy. The advances depend on the data size of gait. However, due to public concerns on privacy and regulations and laws from different countries, it is very difficult and almost impossible to collect a huge centralized gait database for algorithm training. Federated learning is a distributed machine learning technique for privacy-preserving, and can help to solve the problem. We propose a federated gait recognition benchmark, FedGait, to train algorithms using distributed gait data. It is the first benchmark on gait recognition to the best of our knowledge. FedGait can utilizes the gait videos available on multiple clients to learn a robust and generalized model. Based on the real-world gait scenarios, we introduce two federated gait recognition scenarios: institution-based scenario (IBS) and device-based scenario (DBS). Compared with centralized training, federated learning will encounter more serious heterogeneous data and data imbalance problems. We employ four popular databases for experiments, CASIA-B, CASIA-E, ReSGait and OU-MVLP, are involved in FedGait to investigate the problems in federated learning. We hope FedGait is a good start to solve data privacy problem in gait recognition. |
Keywords | |
SUSTech Authorship | Others
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Language | English
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URL | [Source Record] |
Indexed By | |
Funding Project | National Natural Science Foundation of China[61976144]
; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925155017002]
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WOS Research Area | Computer Science
; Engineering
; Imaging Science & Photographic Technology
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WOS Subject | Computer Science, Artificial Intelligence
; Engineering, Electrical & Electronic
; Imaging Science & Photographic Technology
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WOS Accession No | WOS:000897707601053
|
Data Source | IEEE
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PDF url | https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9956474 |
Citation statistics |
Cited Times [WOS]:0
|
Document Type | Conference paper |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/420619 |
Department | Department of Computer Science and Engineering |
Affiliation | 1.College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China 2.Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China |
Recommended Citation GB/T 7714 |
Ziqiong Li,Yan-Ran Li,Shiqi Yu. FedGait: A Benchmark for Federated Gait Recognition[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1371-1377.
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