# GaitContour: Efficient Gait Recognition based on a Contour-Pose Representation

arXiv: https://arxiv.org/abs/2311.16497 · HTML: https://mlsanity.com/paper/2311.16497/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 60 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 30 of 33)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Deep metric learning using Triplet network](https://mlsanity.com/paper/1412.6622/) (2014)
- [Attention Augmented Convolutional Networks](https://mlsanity.com/paper/1904.09925/) (2019)
- [AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time](https://mlsanity.com/paper/2211.03375/) (2022)
- [REPAIR: Removing Representation Bias by Dataset Resampling](https://mlsanity.com/paper/1904.07911/) (2019)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation](https://mlsanity.com/paper/2011.01461/) (2020)
- [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)
- [OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://mlsanity.com/paper/2211.06597/) (2022)
- [Spatial Temporal Transformer Network for Skeleton-based Action Recognition](https://mlsanity.com/paper/2012.06399/) (2020)
- [Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset](https://mlsanity.com/paper/2211.01917/) (2022)
- [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)
- [On Learning Disentangled Representations for Gait Recognition](https://mlsanity.com/paper/1909.03051/) (2019)
- [SkeletonGait: Gait Recognition Using Skeleton Maps](https://mlsanity.com/paper/2311.13444/) (2023)
- [GPGait: Generalized Pose-based Gait Recognition](https://mlsanity.com/paper/2303.05234/) (2023)
- [Spatial Transformer Network on Skeleton-based Gait Recognition](https://mlsanity.com/paper/2204.03873/) (2022)
- [Mimetics: Towards Understanding Human Actions Out of Context](https://mlsanity.com/paper/1912.07249/) (2019)
- [Learning Rich Features for Gait Recognition by Integrating Skeletons and Silhouettes](https://mlsanity.com/paper/2110.13408/) (2021)
- [GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer](https://mlsanity.com/paper/2210.15491/) (2022)

## Built on by (most cited first, 8 of 8)

- [BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models](https://mlsanity.com/paper/2505.18132/) (2025)
- [Silhouette-based Gait Foundation Model](https://mlsanity.com/paper/2512.00691/) (2025)
- [SpheriGait: Enriching Spatial Representation via Spherical Projection for LiDAR-based Gait Recognition](https://mlsanity.com/paper/2409.11869/) (2024)
- [GaitKD: A Universal Decoupled Distillation Framework for Efficient Gait Recognition](https://mlsanity.com/paper/2604.26255/) (2026)
- [MHRGait: Gait Recognition from Momentum Human Rig Pose](https://mlsanity.com/paper/2607.29083/) (2026)
- [ZipGait: Bridging Skeleton and Silhouette with Diffusion Model for Advancing Gait Recognition](https://mlsanity.com/paper/2408.12111/) (2024)
- [HorGait: A Hybrid Model for Accurate Gait Recognition in LiDAR Point Cloud Planar Projections](https://mlsanity.com/paper/2410.08454/) (2024)
- [MMGait: Benchmarking and Unifying Gait Recognition across Heterogeneous Modalities](https://mlsanity.com/paper/2609.11601/) (2026)

## Abstract (arXiv)

Gait recognition holds the promise to robustly identify subjects based on walking patterns instead of appearance information. In recent years, this field has been dominated by learning methods based on two principal input representations: dense silhouette masks or sparse pose keypoints. In this work, we propose a novel, point-based Contour-Pose representation, which compactly expresses both body shape and body parts information. We further propose a local-to-global architecture, called GaitContour, to leverage this novel representation and efficiently compute subject embedding in two stages. The first stage consists of a local transformer that extracts features from five different body regions. The second stage then aggregates the regional features to estimate a global human gait representation. Such a design significantly reduces the complexity of the attention operation and improves efficiency and performance simultaneously. Through large scale experiments, GaitContour is shown to perform significantly better than previous point-based methods, while also being significantly more efficient than silhouette-based methods. On challenging datasets with significant distractors, GaitContour can even outperform silhouette-based methods.