FedGait: A Benchmark for Federated Gait Recognition

Ziqiong Li1; Yan-Ran Li1; Shiqi Yu2

10.1109/ICPR56361.2022.9956474

2022

26th International Conference on Pattern Recognition / 8th International Workshop on Image Mining - Theory and Applications (IMTA)

1051-4651

978-1-6654-9063-4

2022 26th International Conference on Pattern Recognition (ICPR)

1371-1377

21-25 Aug. 2022

Montreal, QC, Canada

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

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.

Training Privacy Data privacy Machine learning algorithms Federated learning Satellite broadcasting Benchmark testing

Others

English

CPCI-S

National Natural Science Foundation of China[61976144] ; Stable Support Plan Program of Shenzhen Natural Science Fund[20200925155017002]

Computer Science ; Engineering ; Imaging Science & Photographic Technology

Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology

WOS:000897707601053

IEEE

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

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.