中文版 | English
Title

FedGait: A Benchmark for Federated Gait Recognition

Author
DOI
Publication Years
2022
Conference Name
26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)
ISSN
1051-4651
ISBN
978-1-6654-9063-4
Source Title
Pages
1371-1377
Conference Date
21-25 Aug. 2022
Conference Place
Montreal, QC, Canada
Publication Place
345 E 47TH ST, NEW YORK, NY 10017 USA
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
Language
English
URL[Source Record]
Indexed By
Funding Project
National Natural Science Foundation of China[61976144] ; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925155017002]
WOS Research Area
Computer Science ; Engineering ; Imaging Science & Photographic Technology
WOS Subject
Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS Accession No
WOS:000897707601053
Data Source
IEEE
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9956474
Citation statistics
Cited Times [WOS]:0
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/420619
DepartmentDepartment 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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Ziqiong Li]'s Articles
[Yan-Ran Li]'s Articles
[Shiqi Yu]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[Ziqiong Li]'s Articles
[Yan-Ran Li]'s Articles
[Shiqi Yu]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Ziqiong Li]'s Articles
[Yan-Ran Li]'s Articles
[Shiqi Yu]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.