# Deep Gait Recognition: A Survey

arXiv: https://arxiv.org/abs/2102.09546 · HTML: https://mlsanity.com/paper/2102.09546/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- Builds on 75 of its 262 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 30 of 75)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Self-Supervised Learning of Pretext-Invariant Representations](https://mlsanity.com/paper/1912.01991/) (2019)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [Deep Visual Domain Adaptation: A Survey](https://mlsanity.com/paper/1802.03601/) (2018)
- [DensePose: Dense Human Pose Estimation In The Wild](https://mlsanity.com/paper/1802.00434/) (2018)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [MoCoGAN: Decomposing Motion and Content for Video Generation](https://mlsanity.com/paper/1707.04993/) (2017)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020)
- [Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2003.14111/) (2020)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Deep Face Recognition: A Survey](https://mlsanity.com/paper/1804.06655/) (2018)
- [On the Detection of Digital Face Manipulation](https://mlsanity.com/paper/1910.01717/) (2019)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace](https://mlsanity.com/paper/1910.04855/) (2019)
- [Emergence of Invariance and Disentanglement in Deep Representations](https://mlsanity.com/paper/1706.01350/) (2017)

## Built on by (most cited first, 30 of 54)

- [Inter-X: Towards Versatile Human-Human Interaction Analysis](https://mlsanity.com/paper/2312.16051/) (2023)
- [OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://mlsanity.com/paper/2211.06597/) (2022)
- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [Spatial Transformer Network on Skeleton-based Gait Recognition](https://mlsanity.com/paper/2204.03873/) (2022)
- [GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition](https://mlsanity.com/paper/2208.01380/) (2022)
- [Hierarchical Spatio-Temporal Representation Learning for Gait Recognition](https://mlsanity.com/paper/2307.09856/) (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)
- [Exploring Transformers for Behavioural Biometrics: A Case Study in Gait Recognition](https://mlsanity.com/paper/2206.01441/) (2022)
- [GaitPT: Skeletons Are All You Need For Gait Recognition](https://mlsanity.com/paper/2308.10623/) (2023)
- [Camera-LiDAR Cross-modality Gait Recognition](https://mlsanity.com/paper/2407.02038/) (2024)
- [Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset](https://mlsanity.com/paper/2407.04190/) (2024)
- [GaitContour: Efficient Gait Recognition based on a Contour-Pose Representation](https://mlsanity.com/paper/2311.16497/) (2023)
- [GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning](https://mlsanity.com/paper/2310.19418/) (2023)
- [Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models](https://mlsanity.com/paper/2305.13765/) (2023)
- [You Can Run but not Hide: Improving Gait Recognition with Intrinsic Occlusion Type Awareness](https://mlsanity.com/paper/2312.02290/) (2023)
- [Deep Learning in Palmprint Recognition-A Comprehensive Survey](https://mlsanity.com/paper/2501.01166/) (2025)
- [GaitSnippet: Gait Recognition Beyond Unordered Sets and Ordered Sequences](https://mlsanity.com/paper/2508.07782/) (2025)
- [HiH: A Multi-modal Hierarchy in Hierarchy Network for Unconstrained Gait Recognition](https://mlsanity.com/paper/2311.11210/) (2023)
- [GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition](https://mlsanity.com/paper/2305.19700/) (2023)
- [Distillation-guided Representation Learning for Unconstrained Gait Recognition](https://mlsanity.com/paper/2307.14578/) (2023)
- [The Paradox of Motion: Evidence for Spurious Correlations in Skeleton-based Gait Recognition Models](https://mlsanity.com/paper/2402.08320/) (2024)
- [A dataset of high-resolution plantar pressures for gait analysis across varying footwear and walking speeds](https://mlsanity.com/paper/2502.17244/) (2025)
- [Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition](https://mlsanity.com/paper/2209.12364/) (2022)
- [CNTN: Cyclic Noise-tolerant Network for Gait Recognition](https://mlsanity.com/paper/2210.06910/) (2022)
- [HDNet: Hierarchical Dynamic Network for Gait Recognition using Millimeter-Wave Radar](https://mlsanity.com/paper/2211.00312/) (2022)
- [Evidence-based Match-status-Aware Gait Recognition for Out-of-Gallery Gait Identification](https://mlsanity.com/paper/2211.08007/) (2022)
- [GaitFormer: Revisiting Intrinsic Periodicity for Gait Recognition](https://mlsanity.com/paper/2307.13259/) (2023)
- [Non-contact Multimodal Indoor Human Monitoring Systems: A Survey](https://mlsanity.com/paper/2312.07601/) (2023)
- [GaitMA: Pose-guided Multi-modal Feature Fusion for Gait Recognition](https://mlsanity.com/paper/2407.14812/) (2024)

## Abstract (arXiv)

Gait recognition is an appealing biometric modality which aims to identify individuals based on the way they walk. Deep learning has reshaped the research landscape in this area since 2015 through the ability to automatically learn discriminative representations. Gait recognition methods based on deep learning now dominate the state-of-the-art in the field and have fostered real-world applications. In this paper, we present a comprehensive overview of breakthroughs and recent developments in gait recognition with deep learning, and cover broad topics including datasets, test protocols, state-of-the-art solutions, challenges, and future research directions. We first review the commonly used gait datasets along with the principles designed for evaluating them. We then propose a novel taxonomy made up of four separate dimensions namely body representation, temporal representation, feature representation, and neural architecture, to help characterize and organize the research landscape and literature in this area. Following our proposed taxonomy, a comprehensive survey of gait recognition methods using deep learning is presented with discussions on their performances, characteristics, advantages, and limitations. We conclude this survey with a discussion on current challenges and mention a number of promising directions for future research in gait recognition.