Papers › GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition
GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition
Torben Teepe, Ali Khan, Johannes Gilg, Fabian Herzog, Stefan Hörmann, Gerhard Rigoll
Gait recognition is a promising video-based biometric for identifying individual walking patterns from a long distance. At present, most gait recognition methods use silhouette images to represent a person in each frame. However, silhouette images can lose fine-grained spatial information, and most papers do not regard how to obtain these silhouettes in complex scenes. Furthermore, silhouette images contain not only gait features but also other visual clues that can be recognized. Hence these approaches can not be considered as strict gait recognition. We leverage recent advances in human pose estimation to estimate robust skeleton poses directly from RGB images to bring back model-based gait recognition with a cleaner representation of gait. Thus, we propose GaitGraph that combines skeleton poses with Graph Convolutional Network (GCN) to obtain a modern model-based approach for gait recognition. The main advantages are a cleaner, more elegant extraction of the gait features and the ability to incorporate powerful spatio-temporal modeling using GCN. Experiments on the popular CASIA-B gait dataset show that our method archives state-of-the-art performance in model-based gait recognition. The code and models are publicly available.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multiview Gait Recognition | CASIA-B | GaitGraph | Accuracy (Cross-View, Avg) | 76.3 | #12 of 12 | Archive leaderboard | report |
| Multiview Gait Recognition | CASIA-B | GaitGraph | BG#1-2 | 74.8 | #12 of 12 | Archive leaderboard | report |
| Multiview Gait Recognition | CASIA-B | GaitGraph | CL#1-2 | 66.3 | #12 of 12 | Archive leaderboard | report |
| Multiview Gait Recognition | CASIA-B | GaitGraph | NM#5-6 | 87.7 | #12 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections