PointGait: Boosting End-to-End 3D Gait Recognition with Point Clouds via Spatiotemporal Modeling

Rui Wang1,5; Chuanfu Shen1,2; Chao Fan1,3; George Q. Huang4; Shiqi Yu1,3

10.1109/IJCB57857.2023.10448817

2023

IEEE International Joint Conference on Biometrics (IJCB)

2474-9680

2023 IEEE INTERNATIONAL JOINT CONFERENCE ON BIOMETRICS, IJCB

SEP 25-28, 2023

null,Ljubljana,SLOVENIA

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

IEEE

LiDAR is a new type of sensor used for gait recognition. Previous LiDAR-based state-of-the-art methods mostly exploit gait features from the depth maps generated by projecting point clouds in a 3D-to-2D manner, rather than directly using the raw 3D point data. However, these projection-based methods require an additional preprocessing step, which obstructs the universality of the method among different types of LiDARs. On the other hand, while existing point-based methods have achieved promising results in 3D object recognition, they have underperformed in 3D gait recognition, indicating the presence of a domain gap between coarse-grained 3D object classification and fine-grained 3D pedestrians recognition. By analyzing the success achieved by camera-based methods, we perceive that point-based gait recognition fails mainly because of neglecting to capture local representation. To address this issue, we propose an end-to-end 3D gait recognition framework named PointGait, which can directly capture informative gait features from point cloud data. Specifically, PointGait is a multi-stream model consisting of a Global and Local Gait Feature Extractor to extract holistic and fine-grained spatial features. Besides, a Personalized Motion Extractor is introduced to capture inter-frame motion features. Our experimental results on a LiDAR gait dataset, SUSTech1K, outperform all popular point-based methods, demonstrating the effectiveness and potential of our approach. In conclusion, the proposed PointGait promotes the development of point-based gait recognition by highlighting the importance of incorporating fine-grained spatiotemporal information.

第一

英语

CPCI-S

National Natural Science Foundation of China[61976144] ; National Key Research and Development Program of China[2020AAA0140002]

Computer Science ; Imaging Science & Photographic Technology

Computer Science, Artificial Intelligence ; Imaging Science & Photographic Technology

WOS:001180818700041

Web of Science

1.Department of Computer Science and Engineering, Southern University of Science and Technology
2.Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong
3.Research Institute of Trustworthy Autonomous System, Southern University of Science and Technology
4.Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University
5.WeBank Institute of Financial Technology, Shenzhen Audencia Financial Technology Institute, Shenzhen University

Rui Wang,Chuanfu Shen,Chao Fan,et al. PointGait: Boosting End-to-End 3D Gait Recognition with Point Clouds via Spatiotemporal Modeling[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023.