# JointsGait:A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping

arXiv: https://arxiv.org/abs/2005.08625 · HTML: https://mlsanity.com/paper/2005.08625/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 22 of 22)

- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [GaitPT: Skeletons Are All You Need For Gait Recognition](https://mlsanity.com/paper/2308.10623/) (2023)
- [PsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait](https://mlsanity.com/paper/2308.10631/) (2023)
- [WildGait: Learning Gait Representations from Raw Surveillance Streams](https://mlsanity.com/paper/2105.05528/) (2021)
- [RealGait: Gait Recognition for Person Re-Identification](https://mlsanity.com/paper/2201.04806/) (2022)
- [Keypoint Message Passing for Video-based Person Re-Identification](https://mlsanity.com/paper/2111.08279/) (2021)
- [Combining the Silhouette and Skeleton Data for Gait Recognition](https://mlsanity.com/paper/2202.10645/) (2022)
- [Generalized Inter-class Loss for Gait Recognition](https://mlsanity.com/paper/2210.06779/) (2022)
- [Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2308.06707/) (2023)
- [GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning](https://mlsanity.com/paper/2310.19418/) (2023)
- [The Paradox of Motion: Evidence for Spurious Correlations in Skeleton-based Gait Recognition Models](https://mlsanity.com/paper/2402.08320/) (2024)
- [CLASH: Complementary Learning with Neural Architecture Search for Gait Recognition](https://mlsanity.com/paper/2407.03632/) (2024)
- [Gait-based Age Group Classification with Adaptive Graph Neural Network](https://mlsanity.com/paper/2210.00294/) (2022)
- [HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based Gait Recognition](https://mlsanity.com/paper/2204.10238/) (2022)
- [Towards Complete-View and High-Level Pose-based Gait Recognition](https://mlsanity.com/paper/2209.11577/) (2022)
- [Unsupervised Gait Recognition with Selective Fusion](https://mlsanity.com/paper/2303.10772/) (2023)
- [InvKA: Gait Recognition via Invertible Koopman Autoencoder](https://mlsanity.com/paper/2309.14764/) (2023)
- [Mesh-Gait: A Unified Framework for Gait Recognition Through Multi-Modal Representation Learning from 2D Silhouettes](https://mlsanity.com/paper/2510.10406/) (2025)
- [Model-based gait recognition using graph network on very large population database](https://mlsanity.com/paper/2112.10305/) (2021)
- [Spatio-temporal Gait Feature with Global Distance Alignment](https://mlsanity.com/paper/2203.03376/) (2022)
- [GaitPoint+: A Gait Recognition Network Incorporating Point Cloud Analysis and Recycling](https://mlsanity.com/paper/2404.10213/) (2024)
- [MoME: Estimating Psychological Traits from Gait with Multi-Stage Mixture of Movement Experts](https://mlsanity.com/paper/2510.04654/) (2025)

## Abstract (arXiv)

Gait, as one of unique biometric features, has the advantage of being recognized from a long distance away, can be widely used in public security. Considering 3D pose estimation is more challenging than 2D pose estimation in practice , we research on using 2D joints to recognize gait in this paper, and a new model-based gait recognition method JointsGait is put forward to extract gait information from 2D human body joints. Appearance-based gait recognition algorithms are prevalent before. However, appearance features suffer from external factors which can cause drastic appearance variations, e.g. clothing. Unlike previous approaches, JointsGait firstly extracted spatio-temporal features from 2D joints using gait graph convolutional networks, which are less interfered by external factors. Secondly, Joints Relationship Pyramid Mapping (JRPM) are proposed to map spatio-temporal gait features into a discriminative feature space with biological advantages according to the relationship of human joints when people are walking at various scales. Finally, we design a fusion loss strategy to help the joints features to be insensitive to cross-view. Our method is evaluated on two large datasets, Kinect Gait Biometry Dataset and CASIA-B. On Kinect Gait Biometry Dataset database, JointsGait only uses corresponding 2D coordinates of joints, but achieves satisfactory recognition accuracy compared with those model-based algorithms using 3D joints. On CASIA-B database, the proposed method greatly outperforms advanced model-based methods in all walking conditions, even performs superior to state-of-art appearance-based methods when clothing seriously affect people's appearance. The experimental results demonstrate that JointsGait achieves the state-of-art performance despite the low dimensional feature (2D body joints) and is less affected by the view variations and clothing variation.