# DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition

arXiv: https://arxiv.org/abs/2303.14953 · HTML: https://mlsanity.com/paper/2303.14953/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 72 references that are in the map.
- First posted on arXiv 27 Mar 2023.

## Builds on (most cited first, 30 of 35)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Learning Face Representation from Scratch](https://mlsanity.com/paper/1411.7923/) (2014)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition](https://mlsanity.com/paper/2004.00288/) (2020)
- [Learning Discriminative Features with Multiple Granularities for Person Re-Identification](https://mlsanity.com/paper/1804.01438/) (2018)
- [Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro](https://mlsanity.com/paper/1701.07717/) (2017)
- [The MegaFace Benchmark: 1 Million Faces for Recognition at Scale](https://mlsanity.com/paper/1512.00596/) (2015)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [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)
- [3D Human Pose Estimation = 2D Pose Estimation + Matching](https://mlsanity.com/paper/1612.06524/) (2016)
- [OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://mlsanity.com/paper/2211.06597/) (2022)
- [Horizontal Pyramid Matching for Person Re-identification](https://mlsanity.com/paper/1804.05275/) (2018)
- [AlignedReID: Surpassing Human-Level Performance in Person Re-Identification](https://mlsanity.com/paper/1711.08184/) (2017)
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://mlsanity.com/paper/2102.03247/) (2021)
- [LidarGait: Benchmarking 3D Gait Recognition with Point Clouds](https://mlsanity.com/paper/2211.10598/) (2022)
- [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)
- [Gait Recognition in the Wild with Multi-hop Temporal Switch](https://mlsanity.com/paper/2209.00355/) (2022)
- [Gait Recognition with Mask-based Regularization](https://mlsanity.com/paper/2203.04038/) (2022)
- [Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification](https://mlsanity.com/paper/1710.00478/) (2017)
- [GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework](https://mlsanity.com/paper/2203.03966/) (2022)

## Built on by (most cited first, 30 of 35)

- [KeyPoint Relative Position Encoding for Face Recognition](https://mlsanity.com/paper/2403.14852/) (2024)
- [QAGait: Revisit Gait Recognition from a Quality Perspective](https://mlsanity.com/paper/2401.13531/) (2024)
- [OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality](https://mlsanity.com/paper/2405.09138/) (2024)
- [Causality-inspired Discriminative Feature Learning in Triple Domains for Gait Recognition](https://mlsanity.com/paper/2407.12519/) (2024)
- [Open-Set Biometrics: Beyond Good Closed-Set Models](https://mlsanity.com/paper/2407.16133/) (2024)
- [It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment](https://mlsanity.com/paper/2411.10742/) (2024)
- [HAMoBE: Hierarchical and Adaptive Mixture of Biometric Experts for Video-based Person ReID](https://mlsanity.com/paper/2508.05038/) (2025)
- [Free Lunch for Gait Recognition: A Novel Relation Descriptor](https://mlsanity.com/paper/2308.11487/) (2023)
- [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)
- [CLASH: Complementary Learning with Neural Architecture Search for Gait Recognition](https://mlsanity.com/paper/2407.03632/) (2024)
- [CarGait: Cross-Attention based Re-ranking for Gait recognition](https://mlsanity.com/paper/2503.03501/) (2025)
- [TrackletGait: A Robust Framework for Gait Recognition in the Wild](https://mlsanity.com/paper/2508.02143/) (2025)
- [Watch Where You Move: Region-aware Dynamic Aggregation and Excitation for Gait Recognition](https://mlsanity.com/paper/2510.16541/) (2025)
- [Silhouette-based Gait Foundation Model](https://mlsanity.com/paper/2512.00691/) (2025)
- [InvKA: Gait Recognition via Invertible Koopman Autoencoder](https://mlsanity.com/paper/2309.14764/) (2023)
- [GaitSTR: Gait Recognition with Sequential Two-stream Refinement](https://mlsanity.com/paper/2404.02345/) (2024)
- [Accurate Quantization for Gait Representation Learning](https://mlsanity.com/paper/2405.13859/) (2024)
- [SpheriGait: Enriching Spatial Representation via Spherical Projection for LiDAR-based Gait Recognition](https://mlsanity.com/paper/2409.11869/) (2024)
- [GaitAdapt: Continual Learning for Evolving Gait Recognition](https://mlsanity.com/paper/2508.03375/) (2025)
- [Gait Recognition via Collaborating Discriminative and Generative Diffusion Models](https://mlsanity.com/paper/2511.06245/) (2025)
- [MHRGait: Gait Recognition from Momentum Human Rig Pose](https://mlsanity.com/paper/2607.29083/) (2026)
- [Vocabulary-Guided Gait Recognition](https://mlsanity.com/paper/2609.18413/) (2026)
- [Occluded Gait Recognition with Mixture of Experts: An Action Detection Perspective](https://mlsanity.com/paper/2609.18432/) (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)
- [Unsupervised Domain Adaptation with Dynamic Clustering and Contrastive Refinement for Gait Recognition](https://mlsanity.com/paper/2501.16608/) (2025)
- [GaitCrafter: Diffusion Model for Biometric Preserving Gait Synthesis](https://mlsanity.com/paper/2508.13300/) (2025)
- [MoME: Estimating Psychological Traits from Gait with Multi-Stage Mixture of Movement Experts](https://mlsanity.com/paper/2510.04654/) (2025)
- [Learning Geometric Invariance for Gait Recognition](https://mlsanity.com/paper/2601.05604/) (2026)

## Abstract (arXiv)

Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. Compared with other biometric technologies, gait recognition is more difficult to disguise and can be applied to the condition of long-distance without the cooperation of subjects. Thus, it has unique potential and wide application for crime prevention and social security. At present, most gait recognition methods directly extract features from the video frames to establish representations. However, these architectures learn representations from different features equally but do not pay enough attention to dynamic features, which refers to a representation of dynamic parts of silhouettes over time (e.g. legs). Since dynamic parts of the human body are more informative than other parts (e.g. bags) during walking, in this paper, we propose a novel and high-performance framework named DyGait. This is the first framework on gait recognition that is designed to focus on the extraction of dynamic features. Specifically, to take full advantage of the dynamic information, we propose a Dynamic Augmentation Module (DAM), which can automatically establish spatial-temporal feature representations of the dynamic parts of the human body. The experimental results show that our DyGait network outperforms other state-of-the-art gait recognition methods. It achieves an average Rank-1 accuracy of 71.4% on the GREW dataset, 66.3% on the Gait3D dataset, 98.4% on the CASIA-B dataset and 98.3% on the OU-MVLP dataset.