# GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition

arXiv: https://arxiv.org/abs/1811.06186 · HTML: https://mlsanity.com/paper/1811.06186/

## Key facts

- Cited by 138 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 30 of 138)

- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [Gait Recognition in the Wild with Dense 3D Representations and A Benchmark](https://mlsanity.com/paper/2204.02569/) (2022)
- [Clothes-Changing Person Re-identification with RGB Modality Only](https://mlsanity.com/paper/2204.06890/) (2022)
- [OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://mlsanity.com/paper/2211.06597/) (2022)
- [Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization](https://mlsanity.com/paper/2103.15537/) (2021)
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://mlsanity.com/paper/2102.03247/) (2021)
- [Towards a Deeper Understanding of Skeleton-based Gait Recognition](https://mlsanity.com/paper/2204.07855/) (2022)
- [LidarGait: Benchmarking 3D Gait Recognition with Point Clouds](https://mlsanity.com/paper/2211.10598/) (2022)
- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality](https://mlsanity.com/paper/2203.03972/) (2022)
- [Exploring Deep Models for Practical Gait Recognition](https://mlsanity.com/paper/2303.03301/) (2023)
- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [GPGait: Generalized Pose-based Gait Recognition](https://mlsanity.com/paper/2303.05234/) (2023)
- [KeyPoint Relative Position Encoding for Face Recognition](https://mlsanity.com/paper/2403.14852/) (2024)
- [DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition](https://mlsanity.com/paper/2303.14953/) (2023)
- [Spatial Transformer Network on Skeleton-based Gait Recognition](https://mlsanity.com/paper/2204.03873/) (2022)
- [Parsing is All You Need for Accurate Gait Recognition in the Wild](https://mlsanity.com/paper/2308.16739/) (2023)
- [GaitGCI: Generative Counterfactual Intervention for Gait Recognition](https://mlsanity.com/paper/2306.03428/) (2023)
- [Learning Rich Features for Gait Recognition by Integrating Skeletons and Silhouettes](https://mlsanity.com/paper/2110.13408/) (2021)
- [GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition](https://mlsanity.com/paper/2208.01380/) (2022)
- [Gait Recognition in the Wild with Multi-hop Temporal Switch](https://mlsanity.com/paper/2209.00355/) (2022)
- [Cross-Covariate Gait Recognition: A Benchmark](https://mlsanity.com/paper/2312.14404/) (2023)
- [Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions](https://mlsanity.com/paper/2404.11023/) (2024)
- [Hierarchical Spatio-Temporal Representation Learning for Gait Recognition](https://mlsanity.com/paper/2307.09856/) (2023)
- [Cross-Platform Video Person ReID: A New Benchmark Dataset and Adaptation Approach](https://mlsanity.com/paper/2408.07500/) (2024)
- [Gait Recognition in Large-scale Free Environment via Single LiDAR](https://mlsanity.com/paper/2211.12371/) (2022)
- [QAGait: Revisit Gait Recognition from a Quality Perspective](https://mlsanity.com/paper/2401.13531/) (2024)
- [Physical Adversarial Attacks for Surveillance: A Survey](https://mlsanity.com/paper/2305.01074/) (2023)
- [MetaGait: Learning to Learn an Omni Sample Adaptive Representation for Gait Recognition](https://mlsanity.com/paper/2306.03445/) (2023)
- [OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality](https://mlsanity.com/paper/2405.09138/) (2024)

## Abstract (arXiv)

As a unique biometric feature that can be recognized at a distance, gait has broad applications in crime prevention, forensic identification and social security. To portray a gait, existing gait recognition methods utilize either a gait template, where temporal information is hard to preserve, or a gait sequence, which must keep unnecessary sequential constraints and thus loses the flexibility of gait recognition. In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames. We propose a new network named GaitSet to learn identity information from the set. Based on the set perspective, our method is immune to permutation of frames, and can naturally integrate frames from different videos which have been filmed under different scenarios, such as diverse viewing angles, different clothes/carrying conditions. Experiments show that under normal walking conditions, our single-model method achieves an average rank-1 accuracy of 95.0% on the CASIA-B gait dataset and an 87.1% accuracy on the OU-MVLP gait dataset. These results represent new state-of-the-art recognition accuracy. On various complex scenarios, our model exhibits a significant level of robustness. It achieves accuracies of 87.2% and 70.4% on CASIA-B under bag-carrying and coat-wearing walking conditions, respectively. These outperform the existing best methods by a large margin. The method presented can also achieve a satisfactory accuracy with a small number of frames in a test sample, e.g., 82.5% on CASIA-B with only 7 frames. The source code has been released at https://github.com/AbnerHqC/GaitSet.