# Learning Rich Features for Gait Recognition by Integrating Skeletons and Silhouettes

arXiv: https://arxiv.org/abs/2110.13408 · HTML: https://mlsanity.com/paper/2110.13408/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 67 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 22 of 22)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [RMPE: Regional Multi-person Pose Estimation](https://mlsanity.com/paper/1612.00137/) (2016)
- [Person Re-identification: Past, Present and Future](https://mlsanity.com/paper/1610.02984/) (2016)
- [Deep Learning Face Representation by Joint Identification-Verification](https://mlsanity.com/paper/1406.4773/) (2014)
- [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)
- [3D Human Pose Estimation = 2D Pose Estimation + Matching](https://mlsanity.com/paper/1612.06524/) (2016)
- [Gait Recognition via Disentangled Representation Learning](https://mlsanity.com/paper/1904.04925/) (2019)
- [GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set](https://mlsanity.com/paper/2102.03247/) (2021)
- [On Learning Disentangled Representations for Gait Recognition](https://mlsanity.com/paper/1909.03051/) (2019)
- [Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos](https://mlsanity.com/paper/2104.08241/) (2021)
- [View-invariant Deep Architecture for Human Action Recognition using late fusion](https://mlsanity.com/paper/1912.03632/) (2019)
- [Deep Heterogeneous Feature Fusion for Template-Based Face Recognition](https://mlsanity.com/paper/1702.04471/) (2017)
- [Sequential convolutional network for behavioral pattern extraction in gait recognition](https://mlsanity.com/paper/2104.11473/) (2021)
- [Covariate conscious approach for Gait recognition based upon Zernike moment invariants](https://mlsanity.com/paper/1611.06683/) (2016)
- [CHD:Consecutive Horizontal Dropout for Human Gait Feature Extraction](https://mlsanity.com/paper/1910.05039/) (2019)

## Built on by (most cited first, 29 of 29)

- [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)
- [BigGait: Learning Gait Representation You Want by Large Vision Models](https://mlsanity.com/paper/2402.19122/) (2024)
- [SkeletonGait: Gait Recognition Using Skeleton Maps](https://mlsanity.com/paper/2311.13444/) (2023)
- [GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition](https://mlsanity.com/paper/2208.01380/) (2022)
- [BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models](https://mlsanity.com/paper/2505.18132/) (2025)
- [Gait Recognition in Large-scale Free Environment via Single LiDAR](https://mlsanity.com/paper/2211.12371/) (2022)
- [OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality](https://mlsanity.com/paper/2405.09138/) (2024)
- [On Denoising Walking Videos for Gait Recognition](https://mlsanity.com/paper/2505.18582/) (2025)
- [Multi-Modal Human Authentication Using Silhouettes, Gait and RGB](https://mlsanity.com/paper/2210.04050/) (2022)
- [Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2308.06707/) (2023)
- [GaitContour: Efficient Gait Recognition based on a Contour-Pose Representation](https://mlsanity.com/paper/2311.16497/) (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)
- [TriGait: Aligning and Fusing Skeleton and Silhouette Gait Data via a Tri-Branch Network](https://mlsanity.com/paper/2308.13340/) (2023)
- [GaitMA: Pose-guided Multi-modal Feature Fusion for Gait Recognition](https://mlsanity.com/paper/2407.14812/) (2024)
- [Silhouette-based Gait Foundation Model](https://mlsanity.com/paper/2512.00691/) (2025)
- [EventGait: Towards Robust Gait Recognition with Event Streams](https://mlsanity.com/paper/2605.22139/) (2026)
- [LiCAF: LiDAR-Camera Asymmetric Fusion for Gait Recognition](https://mlsanity.com/paper/2406.12355/) (2024)
- [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)
- [Gait Recognition via Collaborating Discriminative and Generative Diffusion Models](https://mlsanity.com/paper/2511.06245/) (2025)
- [Beyond Motion Pattern: An Empirical Study of Physical Forces for Human Motion Understanding](https://mlsanity.com/paper/2512.20451/) (2025)
- [Vocabulary-Guided Gait Recognition](https://mlsanity.com/paper/2609.18413/) (2026)
- [GaitPoint+: A Gait Recognition Network Incorporating Point Cloud Analysis and Recycling](https://mlsanity.com/paper/2404.10213/) (2024)
- [ZipGait: Bridging Skeleton and Silhouette with Diffusion Model for Advancing Gait Recognition](https://mlsanity.com/paper/2408.12111/) (2024)
- [Unsupervised Domain Adaptation with Dynamic Clustering and Contrastive Refinement for Gait Recognition](https://mlsanity.com/paper/2501.16608/) (2025)
- [Affinity Contrastive Learning for Skeleton-based Human Activity Understanding](https://mlsanity.com/paper/2601.16694/) (2026)
- [Gait Recognition via Deep Residual Networks and Multi-Branch Feature Fusion](https://mlsanity.com/paper/2604.27353/) (2026)
- [Learning A Unified Template for Gait Recognition](https://mlsanity.com/paper/2609.18490/) (2026)

## Abstract (arXiv)

Gait recognition captures gait patterns from the walking sequence of an individual for identification. Most existing gait recognition methods learn features from silhouettes or skeletons for the robustness to clothing, carrying, and other exterior factors. The combination of the two data modalities, however, is not fully exploited. Previous multimodal gait recognition methods mainly employ the skeleton to assist the local feature extraction where the intrinsic discrimination of the skeleton data is ignored. This paper proposes a simple yet effective Bimodal Fusion (BiFusion) network which mines discriminative gait patterns in skeletons and integrates with silhouette representations to learn rich features for identification. Particularly, the inherent hierarchical semantics of body joints in a skeleton is leveraged to design a novel Multi-Scale Gait Graph (MSGG) network for the feature extraction of skeletons. Extensive experiments on CASIA-B and OUMVLP demonstrate both the superiority of the proposed MSGG network in modeling skeletons and the effectiveness of the bimodal fusion for gait recognition. Under the most challenging condition of walking in different clothes on CASIA-B, our method achieves the rank-1 accuracy of 92.1%.