# Human Gait Recognition using Deep Learning: A Comprehensive Review

arXiv: https://arxiv.org/abs/2309.10144 · HTML: https://mlsanity.com/paper/2309.10144/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 190 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [CSGNet: Neural Shape Parser for Constructive Solid Geometry](https://mlsanity.com/paper/1712.08290/) (2017)
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://mlsanity.com/paper/2102.03247/) (2021)
- [Deep Gait Recognition: A Survey](https://mlsanity.com/paper/2102.09546/) (2021)
- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [Gait Recognition Based on Deep Learning: A Survey](https://mlsanity.com/paper/2201.03323/) (2022)
- [Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition](https://mlsanity.com/paper/1810.07599/) (2018)
- [Leveraging Image Complexity in Macro-Level Neural Network Design for Medical Image Segmentation](https://mlsanity.com/paper/2112.11065/) (2021)
- [GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning](https://mlsanity.com/paper/2306.04650/) (2023)
- [New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis](https://mlsanity.com/paper/2110.15383/) (2021)
- [Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models](https://mlsanity.com/paper/2305.13765/) (2023)
- [NPT-Loss: A Metric Loss with Implicit Mining for Face Recognition](https://mlsanity.com/paper/2103.03503/) (2021)
- [Human Gait Recognition Using Bag of Words Feature Representation Method](https://mlsanity.com/paper/2203.13317/) (2022)
- [Robust Analytics for Video-Based Gait Biometrics](https://mlsanity.com/paper/2111.06670/) (2021)

## Abstract (arXiv)

Gait recognition (GR) is a growing biometric modality used for person identification from a distance through visual cameras. GR provides a secure and reliable alternative to fingerprint and face recognition, as it is harder to distinguish between false and authentic signals. Furthermore, its resistance to spoofing makes GR suitable for all types of environments. With the rise of deep learning, steadily improving strides have been made in GR technology with promising results in various contexts. As video surveillance becomes more prevalent, new obstacles arise, such as ensuring uniform performance evaluation across different protocols, reliable recognition despite shifting lighting conditions, fluctuations in gait patterns, and protecting privacy.This survey aims to give an overview of GR and analyze the environmental elements and complications that could affect it in comparison to other biometric recognition systems. The primary goal is to examine the existing deep learning (DL) techniques employed for human GR that may generate new research opportunities.