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https://github.com/XKMar/Awesome-gait-recognition
A collection of AWESOME things about Gait Recognition
https://github.com/XKMar/Awesome-gait-recognition
List: Awesome-gait-recognition
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A collection of AWESOME things about Gait Recognition
- Host: GitHub
- URL: https://github.com/XKMar/Awesome-gait-recognition
- Owner: XKMar
- Created: 2023-02-27T11:40:44.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-29T12:15:58.000Z (5 months ago)
- Last Synced: 2024-11-21T12:02:14.733Z (about 1 month ago)
- Topics: awesome, awesome-list
- Homepage:
- Size: 68.4 KB
- Stars: 28
- Watchers: 3
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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- ultimate-awesome - Awesome-gait-recognition - A collection of AWESOME things about Gait Recognition. (Other Lists / Monkey C Lists)
README
# Awesome-gait-recognition [](https://github.com/sindresorhus/awesome)
This repo is a collection of **AWESOME** algorithms about **Gait Recognition**, including papers, code, etc.
Feel free to star and fork.
## Table of Contents (Ongoing updates)
- [Awesome-gait-recognition ](#awesome-gait-recognition--)
- [Table of Contents (Ongoing updates)](#table-of-contents-ongoing-updates)
- [1. Survey](#1-survey)
- [2. arXiv](#2-arxiv)
- [3. Conference](#3-conference)
- [1. CVPR](#1-cvpr)
- [2. ICCV](#2-iccv)
- [3. AAAI](#3-aaai)
- [4. ACM MM](#4-acm-mm)
- [5. ECCV](#5-eccv)
- [6. ICME](#6-icme)
- [7. Other Top Conferences](#7-other-top-conferences)
- [4. Journal](#4-journal)
- [1. TPAMI](#1-tpami)
- [2. TIP](#2-tip)
- [3. TIFS](#3-tifs)
- [4. PR](#4-pr)
- [5. TMM](#5-tmm)
- [6. TCSVT](#6-tcsvt)
- [7. TBIOM](#7-tbiom)
- [8. Other Top Journals](#8-other-top-journals)
## 1. Survey
- Deep Gait Recognition: A Survey. \[[TPAMI-23](https://www.computer.org/csdl/journal/tp/2023/01/09714177/1B0XQYUe8Ao)\]\[[arXiv-22](https://arxiv.org/pdf/2102.09546.pdf)\]
- A comprehensive survey on deep gait recognition: algorithms, datasets and challenges. \[[arXiv-22](https://arxiv.org/abs/2206.13732)\]
- A survey on gait recognition via wearable sensors. \[[CSUR-19](https://dl.acm.org/doi/abs/10.1145/3340293)\]
- A survey on gait recognition. \[[CSUR-18](https://dl.acm.org/doi/abs/10.1145/3230633)\]
- Biometric recognition by gait: A survey of modalities and features. \[[CVIU-18](https://www.sciencedirect.com/science/article/pii/S1077314218300079)\]
## 2. arXiv
- GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition. \[[arXiv-23](https://arxiv.org/pdf/2305.19700)\] 
- GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition. \[[arXiv-23](https://arxiv.org/abs/2301.13384)\] 
- Human Body Pose Estimation for Gait Identification: A Comprehensive Survey of Datasets and Models. \[[arXiv-23](https://arxiv.org/pdf/2305.13765)\]
- GaitRef: Gait Recognition with Refined Sequential Skeletons. \[[arXiv-23](https://arxiv.org/pdf/2304.07916)\]
- Unsupervised Gait Recognition with Selective Fusion. \[[arXiv-23](https://arxiv.org/pdf/2303.10772)\] 
- GPGait: Generalized Pose-based Gait Recognition. \[[arXiv-23](https://arxiv.org/pdf/2303.05234)\]
- GaitEditer: Attribute Editing for Gait Representation Learning. \[[arXiv-23](https://arxiv.org/pdf/2303.05076)\] 
- Exploring Deep Models for Practical Gait Recognition. \[[arXiv-23](https://arxiv.org/pdf/2303.03301)\] 
- GaitSADA: Self-Aligned Domain Adaptation for mmWave Gait Recognition. \[[arXiv-23](https://arxiv.org/pdf/2301.13384)\] 
- Parkinson gait modelling from an anomaly deep representation. \[[arXiv-23](https://arxiv.org/pdf/2301.11418)\]
- Combining the Silhouette and Skeleton Data for Gait Recognition \[[arXiv-23](https://arxiv.org/abs/2202.10645v3)\]
- LiCamGait: Gait Recognition in the Wild by Using LiDAR and Camera Multi-modal Visual Sensors. \[[arXiv-22](https://arxiv.org/pdf/2211.12371)\] 
- From Indoor To Outdoor: Unsupervised Domain Adaptive Gait Recognition. \[[arXiv-22](https://arxiv.org/pdf/2211.11155)\] 
- Uncertainty-aware Gait Recognition via Learning from Dirichlet Distribution-based Evidence. \[[arXiv-22](https://arxiv.org/pdf/2211.08007)\] 
- HDNet: Hierarchical Dynamic Network for Gait Recognition using Millimeter-Wave Radar. \[[arXiv-22](https://arxiv.org/pdf/2211.00312)\]
- GaitMixer: Skeleton-based Gait Representation Learning via Wide-spectrum Multi-axial Mixer. \[[arXiv-22](https://arxiv.org/pdf/2210.15491)\]
- Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning. \[[arXiv-22](https://arxiv.org/pdf/2210.11817)\]
- Multi-view Gait Recognition based on Siamese Vision Transformer. \[[arXiv-22](https://arxiv.org/pdf/2210.10421)\]
- CNTN: Cyclic Noise-tolerant Network for Gait Recognition. \[[arXiv-22](https://arxiv.org/pdf/2210.06910)\]
- Generalized Inter-class Loss for Gait Recognition. \[[arXiv-22](https://arxiv.org/pdf/2210.06779)\]
- Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark. \[[arXiv-22](https://arxiv.org/abs/2206.13964)\]
- Spatial Transformer Network on Skeleton-based Gait Recognition. \[[arXiv-22](https://arxiv.org/abs/2204.03873)\]
- SkeletonGait: Gait Recognition Using Skeleton Maps. \[[arXiv-23](https://arxiv.org/pdf/2311.13444v2.pdf)\]
## 3. Conference
### 1. CVPR
- Learning Visual Prompt for Gait Recognition.\[[CVPR-24](https://openaccess.thecvf.com/content/CVPR2024/papers/Ma_Learning_Visual_Prompt_for_Gait_Recognition_CVPR_2024_paper.pdf)\] 
- BigGait: Learning Gait Representation You Want by Large Vision Models. \[[CVPR-24](https://openaccess.thecvf.com/content/CVPR2024/papers/Ye_BigGait_Learning_Gait_Re-presentation_You_Want_by_Large_Vision_Models_CVPR_2024_paper.pdf)\] \[[Github](https://github.com/ShiqiYu/OpenGait)\]
- Dynamic Aggregated Network for Gait Recognition. \[[CVPR-23](https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_Dynamic_Aggregated_Network_for_Gait_Recognition_CVPR_2023_paper.pdf)\] \[[Github](https://github.com/XKMar/FastGait)\] 
- An In-Depth Exploration of Person Re-Identification and Gait Recognition in Cloth-Changing Conditions. \[[CVPR-23](https://openaccess.thecvf.com/content/CVPR2023/papers/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.pdf)\] \[[Github](https://github.com/BNU-IVC/CCPG)\]
- GaitGCI: Generative Counterfactual Intervention for Gait Recognition.\[[CVPR-23](https://openaccess.thecvf.com/content/CVPR2023/papers/Li_An_In-Depth_Exploration_of_Person_Re-Identification_and_Gait_Recognition_in_CVPR_2023_paper.pdf)\] 
- LidarGait: Benchmarking 3D Gait Recognition With Point Clouds. \[[CVPR-23](https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_LidarGait_Benchmarking_3D_Gait_Recognition_With_Point_Clouds_CVPR_2023_paper.pdf)\] \[[Github](https://lidargait.github.io/)\]
- OpenGait: Revisiting Gait Recognition Towards Better Practicality. \[[CVPR-23](https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf)\]\[[Github](https://github.com/ShiqiYu/OpenGait)\] 
- Multi-Modal Gait Recognition via Effective Spatial-Temporal Feature Fusion. \[[CVPR-23](https://openaccess.thecvf.com/content/CVPR2023/papers/Cui_Multi-Modal_Gait_Recognition_via_Effective_Spatial-Temporal_Feature_Fusion_CVPR_2023_paper.pdf)\]
- Gait Recognition in the Wild With Dense 3D Representations and a Benchmark. \[[CVPR-22](https://openaccess.thecvf.com/content/CVPR2022/papers/Zheng_Gait_Recognition_in_the_Wild_With_Dense_3D_Representations_and_CVPR_2022_paper.pdf)\] \[[Github](https://gait3d.github.io/)\]
- Lagrange Motion Analysis and View Embeddings for Improved Gait Recognition.\[[CVPR-22](https://openaccess.thecvf.com/content/CVPR2022/papers/Chai_Lagrange_Motion_Analysis_and_View_Embeddings_for_Improved_Gait_Recognition_CVPR_2022_paper.pdf)\] 
- Cloth-Changing Person Re-Identification From a Single Image With Gait Prediction and Regularization. \[[CVPR-22](https://openaccess.thecvf.com/content/CVPR2022/papers/Jin_Cloth-Changing_Person_Re-Identification_From_a_Single_Image_With_Gait_Prediction_CVPR_2022_paper.pdf)\]
- Towards a Deeper Understanding of Skeleton-based Gait Recognition. \[[CVPRW-22](https://openaccess.thecvf.com/content/CVPR2022W/Biometrics/html/Teepe_Towards_a_Deeper_Understanding_of_Skeleton-Based_Gait_Recognition_CVPRW_2022_paper.html)\] 
- Cross-View Gait Recognition With Deep Universal Linear Embeddings. \[[CVPR-21](https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_Cross-View_Gait_Recognition_With_Deep_Universal_Linear_Embeddings_CVPR_2021_paper.pdf)\]
- Gait Recognition via Semi-supervised Disentangled Representation Learning to Identity and Covariate Features. \[[CVPR-20](https://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Gait_Recognition_via_Semi-supervised_Disentangled_Representation_Learning_to_Identity_and_CVPR_2020_paper.pdf)\]
- GaitPart: Temporal Part-Based Model for Gait Recognition. \[[CVPR-20](https://openaccess.thecvf.com/content_CVPR_2020/papers/Fan_GaitPart_Temporal_Part-Based_Model_for_Gait_Recognition_CVPR_2020_paper.pdf)\]
- A geometric convnet on 3d shape manifold for gait recognition. \[[CVPRW-20](http://openaccess.thecvf.com/content_CVPRW_2020/html/w50/Hosni_A_Geometric_ConvNet_on_3D_Shape_Manifold_for_Gait_Recognition_CVPRW_2020_paper.html)\]
- Learning Joint Gait Representation via Quintuplet Loss Minimization. \[[CVPR-19](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Learning_Joint_Gait_Representation_via_Quintuplet_Loss_Minimization_CVPR_2019_paper.pdf)\]
- Gait Recognition via Disentangled Representation Learning. \[[CVPR-19](https://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Gait_Recognition_via_Disentangled_Representation_Learning_CVPR_2019_paper.pdf)\]
- EV-Gait: Event-Based Robust Gait Recognition Using Dynamic Vision Sensors. \[[CVPR-19](https://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_EV-Gait_Event-Based_Robust_Gait_Recognition_Using_Dynamic_Vision_Sensors_CVPR_2019_paper.pdf)\]
- Joint Intensity and Spatial Metric Learning for Robust Gait Recognition. \[[CVPR-17](https://openaccess.thecvf.com/content_cvpr_2017/papers/Makihara_Joint_Intensity_and_CVPR_2017_paper.pdf)\]
- GaitGAN: Invariant Gait Feature Extraction Using Generative Adversarial Networks. \[[CVPRW-17](https://openaccess.thecvf.com/content_cvpr_2017_workshops/w6/papers/Yu_GaitGAN_Invariant_Gait_CVPR_2017_paper.pdf)\]
- Gait Recognition under Speed Transition. \[[CVPR-14](https://openaccess.thecvf.com/content_cvpr_2014/papers/Mansur_Gait_Recognition_under_2014_CVPR_paper.pdf)\]
- Video from nearly still: An application to low frame-rate gait recognition. \[[CVPR-12](https://ieeexplore.ieee.org/document/6247844)\]
- Gait recognition from time-normalized joint-angle trajectories in the walking plane.\[[CVPR-01](https://ieeexplore.ieee.org/abstract/document/991036/)\]
### 2. ICCV
- GPGait: Generalized Pose-based Gait Recognition. \[[ICCV-23](https://openaccess.thecvf.com/content/ICCV2023/papers/Fu_GPGait_Generalized_Pose-based_Gait_Recognition_ICCV_2023_paper.pdf)\]
- Physics-Augmented Autoencoder for 3D Skeleton-Based Gait Recognition. \[[ICCV-23](https://openaccess.thecvf.com/content/ICCV2023/papers/Guo_Physics-Augmented_Autoencoder_for_3D_Skeleton-Based_Gait_Recognition_ICCV_2023_paper.pdf)\]
- Fine-grained Unsupervised Domain Adaptation for Gait Recognition. \[[ICCV-23](https://openaccess.thecvf.com/content/ICCV2023/papers/Ma_Fine-grained_Unsupervised_Domain_Adaptation_for_Gait_Recognition_ICCV_2023_paper.pdf)\]
- Hierarchical Spatio-Temporal Representation Learning for Gait Recognition. \[[ICCV-23](https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_Hierarchical_Spatio-Temporal_Representation_Learning_for_Gait_Recognition_ICCV_2023_paper.pdf)\]
- DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition. \[[ICCV-23](https://openaccess.thecvf.com/content/ICCV2023/papers/Wang_DyGait_Exploiting_Dynamic_Representations_for_High-performance_Gait_Recognition_ICCV_2023_paper.pdf)\]
- Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation. \[[ICCV-21](https://openaccess.thecvf.com/content/ICCV2021/papers/Lin_Gait_Recognition_via_Effective_Global-Local_Feature_Representation_and_Local_Temporal_ICCV_2021_paper.pdf)\]
- Context-Sensitive Temporal Feature Learning for Gait Recognition. \[[ICCV-21](https://openaccess.thecvf.com/content/ICCV2021/papers/Huang_Context-Sensitive_Temporal_Feature_Learning_for_Gait_Recognition_ICCV_2021_paper.pdf)\]
- 3D Local Convolutional Neural Networks for Gait Recognition. \[[ICCV-21](https://openaccess.thecvf.com/content/ICCV2021/papers/Huang_3D_Local_Convolutional_Neural_Networks_for_Gait_Recognition_ICCV_2021_paper.pdf)\]
- Gait Recognition in the Wild: A Benchmark. \[[ICCV-21](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhu_Gait_Recognition_in_the_Wild_A_Benchmark_ICCV_2021_paper.pdf)\]
- End-to-end Model-based Gait Recognition using Synchronized Multi-view Pose Constraint. \[[ICCVW-21](https://openaccess.thecvf.com/content/ICCV2021W/HTCV/html/Li_End-to-End_Model-Based_Gait_Recognition_Using_Synchronized_Multi-View_Pose_Constraint_ICCVW_2021_paper.html)\]
### 3. AAAI
- QAGait: Revisit Gait Recognition From a Quality Perspective. \[[AAAI-24](https://browse.arxiv.org/pdf/2401.13531.pdf)\]
- Gait Recognition for Co-Existing Multiple People Using Millimeter Wave Sensing. \[[AAAI-20](https://ojs.aaai.org/index.php/AAAI/article/view/5430)\]
- STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits. \[[AAAI-20](https://ojs.aaai.org/index.php/AAAI/article/view/5490)\]
- GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition. \[[AAAI-19](https://ojs.aaai.org/index.php/AAAI/article/view/4821)\]
### 4. ACM MM
- Generalized Inter-class Loss for Gait Recognition. \[[ACM MM-22](https://dl.acm.org/doi/abs/10.1145/3503161.3548311)\]
- Gait recognition in the wild with multi-hop temporal switch. \[[ACM MM-22](https://dl.acm.org/doi/abs/10.1145/3503161.3547897)\]
- Gait recognition with multiple-temporal-scale 3d convolutional neural network. \[[ACM MM-20](https://dl.acm.org/doi/abs/10.1145/3394171.3413861)\]
### 5. ECCV
- MetaGait: Learning to Learn an Omni Sample Adaptive Representation for Gait Recognition. \[[ECCV-22](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136650350.pdf)\]
- GaitEdge: Beyond Plain End-to-End Gait Recognition for Better Practicality. \[[ECCV-22](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123540358.pdf)\]
- Gait Lateral Network: Learning Discriminative and Compact Representations for Gait Recognition. \[[ECCV-22](https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136650368.pdf)\]
- Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping. \[[ECCV-20](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123550154.pdf)\]
- Gait Recognition from a Single Image using a Phase-Aware Gait Cycle Reconstruction Network. \[[ECCV-20](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123640375.pdf)\]
- Gait recognition using a view transformation model in the frequency domain. \[[ECCV-06](https://link.springer.com/chapter/10.1007/11744078_12)\]
### 6. ICME
- Decomposing Identity and View for Cross-View Gait Recognition. \[[ICME-22](https://ieeexplore.ieee.org/abstract/document/9859981/)\]]
- GaitTransformer: Multiple-Temporal-Scale Transformer for Cross-View Gait Recognition. \[[ICME-22](https://ieeexplore.ieee.org/abstract/document/9859928/)\]]
### 7. Other Top Conferences
- You Can Run but not Hide: Improving Gait Recognition with Intrinsic Occlusion Type Awareness. \[[WACV-24](https://openaccess.thecvf.com/content/WACV2024/papers/Gupta_You_Can_Run_but_Not_Hide_Improving_Gait_Recognition_With_WACV_2024_paper.pdf)\]
- Watch Where You Head: A View-Biased Domain Gap in Gait Recognition and Unsupervised Adaptation \[[WACV-24](https://openaccess.thecvf.com/content/WACV2024/papers/Habib_Watch_Where_You_Head_A_View-Biased_Domain_Gap_in_Gait_WACV_2024_paper.pdf)\]
- CTrGAN: Cycle Transformers GAN for Gait Transfer. \[[WACV-23](https://openaccess.thecvf.com/content/WACV2023/papers/Mahpod_CTrGAN_Cycle_Transformers_GAN_for_Gait_Transfer_WACV_2023_paper.pdf)\]
- Long Range Gait Matching Using 3D Body Fitting With Gait-Specific Motion Constraints. \[[WACV-23](https://openaccess.thecvf.com/content/WACV2023W/LRR/html/Segundo_Long_Range_Gait_Matching_Using_3D_Body_Fitting_With_Gait-Specific_WACVW_2023_paper.html)\]
- Gait Recognition Using 3-D Human Body Shape Inference. \[[WACV-23](https://openaccess.thecvf.com/content/WACV2023/html/Zhu_Gait_Recognition_Using_3-D_Human_Body_Shape_Inference_WACV_2023_paper.html)\]
- FedGait: A Benchmark for Federated Gait Recognition. \[[ICPR-22](https://ieeexplore.ieee.org/abstract/document/9956474/)\]
- Interpretable Gait Recognition by Granger Causality. \[[ICPR-22](https://ieeexplore.ieee.org/abstract/document/9956624/)\]
- GAITTAKE: Gait recognition by temporal attention and keypoint-guided embedding.\[[ICIP-22](https://ieeexplore.ieee.org/abstract/document/9897409/)\]
- GaitStrip: Gait Recognition via Effective Strip-based Feature Representations and Multi-Level Framework. \[[ACCV-22](https://openaccess.thecvf.com/content/ACCV2022/papers/Wang_GaitStrip_Gait_Recognition_via_Effective_Strip-based_Feature_Representations_and_Multi-Level_ACCV_2022_paper.pdf)\]
- Silhouette-based view-embeddings for gait recognition under multiple views. \[[ICIP-21](https://ieeexplore.ieee.org/abstract/document/9506238/)\]
- Real-Time Gait-Based Age Estimation and Gender Classification From a Single Image. \[[WACV-21](https://openaccess.thecvf.com/content/WACV2021/papers/Xu_Real-Time_Gait-Based_Age_Estimation_and_Gender_Classification_From_a_Single_WACV_2021_paper.pdf)\]
- GaitMask: Mask-based Model for Gait Recognition.\[[BMVC-21](https://www.bmvc2021-virtualconference.com/assets/papers/0471.pdf)\]
- Gaitgraph: Graph convolutional network for skeleton-based gait recognition. \[[ICIP-21](https://ieeexplore.ieee.org/abstract/document/9506717/)\]
- Part-based collaborative spatio-temporal feature learning for cloth-changing gait recognition. \[[ICPR-20](https://ieeexplore.ieee.org/abstract/document/9412714/)\]
- Gait recognition using multi-scale partial representation transformation with capsules. \[[ICPR-20](https://ieeexplore.ieee.org/abstract/document/9412517/)\]
- End-to-end model-based gait recognition. \[[ACCV-20](https://openaccess.thecvf.com/content/ACCV2020/html/Li_End-to-end_Model-based_Gait_Recognition_ACCV_2020_paper.html)\]
- Gait energy image restoration using generative adversarial networks. \[[ICIP-19](https://ieeexplore.ieee.org/abstract/document/8803236/)\]
- Glidar3dj: a view-invariant gait identification via flash lidar data correction. \[[ICIP-19](https://ieeexplore.ieee.org/abstract/document/8803237/)\]
- Pose-Based Temporal-Spatial Network (PTSN) for Gait Recognition with Carrying and Clothing Variations. \[[Biometric Recognition-17](https://www.semanticscholar.org/paper/Pose-Based-Temporal-Spatial-Network-(PTSN)-for-Gait-Liao-Cao/83deacfb7271717dfafbf10730d2c102dcb4b711)\]
- Multi-view gait recognition using 3D convolutional neural networks. \[[ICIP-16](https://ieeexplore.ieee.org/abstract/document/7533144/)\]
- GEINet: View-invariant gait recognition using a convolutional neural network. \[[ICB-16](https://ieeexplore.ieee.org/abstract/document/7550060/)\]
- Learning effective gait features using LSTM. \[[ICPR-16](https://ieeexplore.ieee.org/abstract/document/7899654/)\]
- Robust view transformation model for gait recognition. \[[ICIP-11](https://ieeexplore.ieee.org/abstract/document/6115889/)\]
- Cross view gait recognition using correlation strength. \[[BMVC-10](http://www.eecs.qmul.ac.uk/~sgg/papers/BashirXiangGong_BMVC2010.pdf)\]
- A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. \[[ICPR-06](https://ieeexplore.ieee.org/abstract/document/1699873/)\]
- automated model-based extraction and analysis of gait. \[[AFGR-04](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1301502)\]
## 4. Journal
### 1. TPAMI
- GaitSet: Cross-view gait recognition through utilizing gait as a deep set. \[[TPAMI-22](https://ieeexplore.ieee.org/abstract/document/9351667/)\]
- On learning disentangled representations for gait recognition. \[[TPAMI-20](https://ieeexplore.ieee.org/abstract/document/9154576/)\]
- Multi-gait recognition based on attribute discovery. \[[TPAMI-17](https://ieeexplore.ieee.org/abstract/document/7976333/)\]
- A comprehensive study on cross-view gait based human identification with deep cnns. \[[TPAMI-16](https://ieeexplore.ieee.org/document/7439821)\]
- Human identification using temporal information preserving gait template. \[[TPAMI-11](https://ieeexplore.ieee.org/abstract/document/6112763/)\]
- Individual recognition using gait energy image. \[[TPAMI-06](https://ieeexplore.ieee.org/abstract/document/1561189/)\]
- Silhouette analysis-based gait recognition for human identification. \[[TPAMI-03](https://ieeexplore.ieee.org/abstract/document/1251144/)\]
### 2. TIP
- GaitMPL: Gait Recognition with Memory-Augmented Progressive Learning. \[[TIP-22](https://ieeexplore.ieee.org/abstract/document/9769988/)\]
- Multi-view gait image generation for cross-view gait recognition. \[[TIP-22](https://ieeexplore.ieee.org/abstract/document/9349211/)\]
- Condition-aware comparison scheme for gait recognition. \[[TIP-20](https://ieeexplore.ieee.org/abstract/document/9275377/)\]
- Cross-view gait recognition by discriminative feature learning. \[[TIP-19](https://ieeexplore.ieee.org/abstract/document/8759096/)\]
- Recognizing gaits across views through correlated motion co-clustering. \[[TIP-14](https://ieeexplore.ieee.org/abstract/document/6680737/)\]
- Gait recognition with shifted energy image and structural feature extraction. \[[TIP-12](https://ieeexplore.ieee.org/abstract/document/6111293/)\]
### 3. TIFS
- Occlusion-aware Human Mesh Model-based Gait Recognition. \[[TIFS-23](https://ieeexplore.ieee.org/abstract/document/10015098/)\]
- GaitReload: A Reloading Framework for Defending Against On-Manifold Adversarial Gait Sequences. \[[TIFS-23](https://ieeexplore.ieee.org/abstract/document/10025772/)\]
- Joint intensity transformer network for gait recognition robust against clothing and carrying status. \[[TIFS-19](https://ieeexplore.ieee.org/abstract/document/8695052/)\]
- Skeleton-based gait recognition via robust frame-level matching. \[[TIFS-19](https://ieeexplore.ieee.org/abstract/document/8653351/)\]
- Multi-task GANs for view-specific feature learning in gait recognition. \[[TIFS-18](https://ieeexplore.ieee.org/abstract/document/8374898/)\]
- Human identification from freestyle walks using posture-based gait feature. \[[TIFS-17](https://ieeexplore.ieee.org/abstract/document/8007293/)\]
- Recognizing gaits on spatio-temporal feature domain. \[[TIFS-14](https://ieeexplore.ieee.org/abstract/document/6849450/)\]
- A new view-invariant feature for cross-view gait recognition.\[[TIFS-13](https://ieeexplore.ieee.org/abstract/document/6478807/)\]
- Gait recognition using compact feature extraction transforms and depth information. \[[TIFS-07](https://ieeexplore.ieee.org/abstract/document/4291549/)\]
### 4. PR
- Temporal sparse adversarial attack on sequence-based gait recognition. \[[PR-23](https://arxiv.org/pdf/2002.09674)\]
- GaitSlice: A gait recognition model based on spatio-temporal slice features. \[[PR-22](https://www.sciencedirect.com/science/article/abs/pii/S0031320321006294)\]
- Gait recognition invariant to carried objects using alpha blending generative adversarial networks. \[[PR-22](https://www.sciencedirect.com/science/article/pii/S0031320320301795)\]
- Symmetry-Driven hyper feature GCN for skeleton-based gait recognition. \[[PR-22](https://www.sciencedirect.com/science/article/pii/S0031320322000012)\]
- A unified perspective of classification-based loss and distance-based loss for cross-view gait recognition. \[[PR-22](https://www.sciencedirect.com/science/article/pii/S0031320321006956)\]
- Multi-task learning for gait-based identity recognition and emotion recognition using attention enhanced temporal graph convolutional network \[[PR-21](https://www.sciencedirect.com/science/article/pii/S0031320321000558)\]
- Pose Gait: A model-based gait recognition method with body pose and human prior knowledge. \[[PR-20](https://www.sciencedirect.com/science/article/pii/S003132031930370X)\]
- Gaitnet: An end-to-end network for gait based human identification. \[[PR-19](https://www.sciencedirect.com/science/article/pii/S0031320319302912)\]
- A comprehensive study on gait biometrics using a joint CNN-based method. \[[PR-19](https://www.sciencedirect.com/science/article/pii/S0031320319301694)\]
- Complete canonical correlation analysis with application to multi-view gait recognition. \[[PR-16](https://www.sciencedirect.com/science/article/pii/S003132031500299X)\]
- Uncooperative gait recognition by learning to rank. \[[PR-14](https://www.sciencedirect.com/science/article/pii/S0031320314002325)\]
- Gait flow image: A silhouette-based gait representation for human identification. \[[PR-11](https://www.sciencedirect.com/science/article/pii/S0031320310004954)\]
- Extracting a diagnostic gait signature. \[[PR-08](https://www.sciencedirect.com/science/article/pii/S003132030700492X)\]
### 5. TMM
- Improving Disentangled Representation Learning for Gait Recognition using Group Supervision. \[[TMM-22](https://ieeexplore.ieee.org/abstract/document/9767609/)\]
- A Strong and Robust Skeleton-based Gait Recognition Method with Gait Periodicity Priors. \[[TMM-22](https://ieeexplore.ieee.org/abstract/document/9721551/)\]
- Associated spatio-temporal capsule network for gait recognition. \[[TMM-21](https://ieeexplore.ieee.org/document/9359526/)\]
- Gait Recognition based on Local Graphical Skeleton Descriptor with Pairwise Similarity Network. \[[TMM-21](https://ieeexplore.ieee.org/abstract/document/9478261/)\]
- Attentive spatial–temporal summary networks for feature learning in irregular gait recognition]\[[TMM-19](https://ieeexplore.ieee.org/abstract/document/8643814/) \]
### 6. TCSVT
- Enhanced Spatial-Temporal Salience for Cross-view Gait Recognition. \[[TCSVT-22](https://ieeexplore.ieee.org/abstract/document/9777726/)\]
- Cross-view gait recognition using pairwise spatial transformer networks. \[[TCSVT-20](https://ieeexplore.ieee.org/abstract/document/9006806/)\]
- Coupled bilinear discriminant projection for cross-view gait recognition. \[[TCSVT-19](https://ieeexplore.ieee.org/abstract/document/8616800/)\]
- On input/output architectures for convolutional neural network-based cross-view gait recognition. \[[TCSVT-17](https://ieeexplore.ieee.org/abstract/document/8063344/)\]
- Gait recognition under various viewing angles based on correlated motion regression. \[[TCSVT-12](https://ieeexplore.ieee.org/abstract/document/6145627/)\]
- Fusion of static and dynamic body biometrics for gait recognition. \[[TCSVT-04](https://ieeexplore.ieee.org/abstract/document/1269749/)\]
### 7. TBIOM
- Gait Pyramid Attention Network: Toward Silhouette Semantic Relation Learning for Gait Recognition. \[[TBIOM-22](https://ieeexplore.ieee.org/abstract/document/9916067/)\]
- STAR: Spatio-Temporal Augmented Relation Network for Gait Recognition. \[[TBIOM-22](https://ieeexplore.ieee.org/abstract/document/9913216/)\]]
- Multi-view large population gait database with human meshes and its performance evaluation. \[[TBIOM-22](https://ieeexplore.ieee.org/abstract/document/9773349/)\]]
- Set residual network for silhouette-based gait recognition. \[[TBIOM-21](https://ieeexplore.ieee.org/abstract/document/9410587/)\]]
- View-invariant gait recognition with attentive recurrent learning of partial representations. \[[TBIOM-20](https://ieeexplore.ieee.org/abstract/document/9229117/)\]]
- Performance evaluation of model-based gait on multi-view very large population database with pose sequences. \[[TBIOM-20](https://ieeexplore.ieee.org/abstract/document/9139355/)\]
### 8. Other Top Journals
- Learning Temporal Attention based Keypoint-guided Embedding for Gait Recognition. \[[JSTSP-23](https://ieeexplore.ieee.org/abstract/document/10113150/)\]
- Gait Quality Aware Network: Toward the Interpretability of Silhouette-Based Gait Recognition. \[[TNNLS-22](https://ieeexplore.ieee.org/abstract/document/9737002/)\]
- Gait Identification Based on Human Skeleton with Pairwise Graph Convolutional Network. \[[ICME-21](https://ieeexplore.ieee.org/abstract/document/9428123/)\]
- Static and Dynamic Features Analysis from Human Skeletons for Gait Recognition. \[[IJCB-21](https://ieeexplore.ieee.org/abstract/document/9484378/)\]
- Gait recognition based on 3d skeleton data and graph convolutional network. \[[IJCB-20](https://ieeexplore.ieee.org/abstract/document/9304916/)\]
- Dense-view geis set: View space covering for gait recognition based on dense-view gan. \[[IJCB-20](https://ieeexplore.ieee.org/abstract/document/9304910/)\]
- DeformGait: Gait Recognition under Posture Changes using Deformation Patterns between Gait Feature Pairs. \[[IJCB-20](https://ieeexplore.ieee.org/abstract/document/9304902/)\]
- Walking speed influences on gait cycle variability. \[[GP-07](https://www.sciencedirect.com/science/article/pii/S0966636206001846)\]