# Gait Recognition Based on Deep Learning: A Survey arXiv: https://arxiv.org/abs/2201.03323 ยท HTML: https://mlsanity.com/paper/2201.03323/ ## Key facts - Cited by 21 papers in the map (data through 14 Oct 2026). - Builds on 18 of its 145 references that are in the map. - First posted on arXiv 10 Jan 2022. ## Builds on (most cited first, 18 of 18) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016) - [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014) - [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014) - [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017) - [Rethinking Pre-training and Self-training](https://mlsanity.com/paper/2006.06882/) (2020) - [Gait Recognition via Disentangled Representation Learning](https://mlsanity.com/paper/1904.04925/) (2019) - [On Learning Disentangled Representations for Gait Recognition](https://mlsanity.com/paper/1909.03051/) (2019) - [Deep Learning-Based Gait Recognition Using Smartphones in the Wild](https://mlsanity.com/paper/1811.00338/) (2018) - [Gait Recognition using Multi-Scale Partial Representation Transformation with Capsules](https://mlsanity.com/paper/2010.09084/) (2020) - [Pose-based Deep Gait Recognition](https://mlsanity.com/paper/1710.06512/) (2017) - [Associated Spatio-Temporal Capsule Network for Gait Recognition](https://mlsanity.com/paper/2101.02458/) (2021) - [Person Identification from Partial Gait Cycle Using Fully Convolutional Neural Network](https://mlsanity.com/paper/1804.08506/) (2018) - [Intestinal Parasites Classification Using Deep Belief Networks](https://mlsanity.com/paper/2101.06747/) (2021) - [Robust Cross-View Gait Recognition with Evidence: A Discriminant Gait GAN (DiGGAN) Approach](https://mlsanity.com/paper/1811.10493/) (2018) ## Built on by (most cited first, 21 of 21) - [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) - [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) - [Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset](https://mlsanity.com/paper/2407.04190/) (2024) - [Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models](https://mlsanity.com/paper/2305.13765/) (2023) - [HiH: A Multi-modal Hierarchy in Hierarchy Network for Unconstrained Gait Recognition](https://mlsanity.com/paper/2311.11210/) (2023) - [A dataset of high-resolution plantar pressures for gait analysis across varying footwear and walking speeds](https://mlsanity.com/paper/2502.17244/) (2025) - [CTrGAN: Cycle Transformers GAN for Gait Transfer](https://mlsanity.com/paper/2206.15248/) (2022) - [ISP meets Deep Learning: A Survey on Deep Learning Methods for Image Signal Processing](https://mlsanity.com/paper/2305.11994/) (2023) - [GaitMorph: Transforming Gait by Optimally Transporting Discrete Codes](https://mlsanity.com/paper/2307.14713/) (2023) - [Realtime Person Identification via Gait Analysis](https://mlsanity.com/paper/2404.15312/) (2024) - [A Phenomenological AI Foundation Model for Physical Signals](https://mlsanity.com/paper/2410.14724/) (2024) - [Developing Normative Gait Cycle Parameters for Clinical Analysis Using Human Pose Estimation](https://mlsanity.com/paper/2411.13716/) (2024) - [MimicGait: A Model Agnostic approach for Occluded Gait Recognition using Correlational Knowledge Distillation](https://mlsanity.com/paper/2501.15666/) (2025) - [DepthGait: Multi-Scale Cross-Level Feature Fusion of RGB-Derived Depth and Silhouette Sequences for Robust Gait Recognition](https://mlsanity.com/paper/2508.03397/) (2025) - [Using simulation to calibrate real data acquisition in veterinary medicine](https://mlsanity.com/paper/2307.11695/) (2023) - [Human Gait Recognition using Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2309.10144/) (2023) - [ExoGait-MS: Learning Periodic Dynamics with Multi-Scale Graph Network for Exoskeleton Gait Recognition](https://mlsanity.com/paper/2505.18018/) (2025) - [DiffCrossGait: Trajectory-Level Alignment for 2D-3D Cross-Modal Gait Recognition via Latent Diffusion](https://mlsanity.com/paper/2606.00153/) (2026) - [Probing Identity-Specific Motion Signatures: A Controlled Diagnostic Study](https://mlsanity.com/paper/2607.03633/) (2026) ## Abstract (arXiv) In general, biometry-based control systems may not rely on individual expected behavior or cooperation to operate appropriately. Instead, such systems should be aware of malicious procedures for unauthorized access attempts. Some works available in the literature suggest addressing the problem through gait recognition approaches. Such methods aim at identifying human beings through intrinsic perceptible features, despite dressed clothes or accessories. Although the issue denotes a relatively long-time challenge, most of the techniques developed to handle the problem present several drawbacks related to feature extraction and low classification rates, among other issues. However, deep learning-based approaches recently emerged as a robust set of tools to deal with virtually any image and computer-vision related problem, providing paramount results for gait recognition as well. Therefore, this work provides a surveyed compilation of recent works regarding biometric detection through gait recognition with a focus on deep learning approaches, emphasizing their benefits, and exposing their weaknesses. Besides, it also presents categorized and characterized descriptions of the datasets, approaches, and architectures employed to tackle associated constraints.