Human Identification at a Distance: Challenges, Methods and Results on the Competition HID 2025
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| Format: | Preprint |
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2026
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| _version_ | 1866908819386269696 |
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| author | Ma, Jingzhe Zhang, Meng Yu, Jianlong Liu, Kun Xu, Zunxiao Cheng, Xue Zhou, Junjie Wang, Yanfei Li, Jiahang Wang, Zepeng Osamura, Kazuki Liu, Rujie Abe, Narishige Wang, Jingjie Zhang, Shunli Xie, Haojun Wu, Jiajun Wu, Weiming Kang, Wenxiong Gao, Qingshuo Xiong, Jiaming Ben, Xianye Chen, Lei Song, Lichen Cui, Junjian Xiong, Haijun Lu, Junhao Feng, Bin Liu, Mengyuan Zhou, Ji Zhao, Baoquan Xu, Ke Huang, Yongzhen Wang, Liang Marin-Jimenez, Manuel J Ahad, Md Atiqur Rahman Yu, Shiqi |
| author_facet | Ma, Jingzhe Zhang, Meng Yu, Jianlong Liu, Kun Xu, Zunxiao Cheng, Xue Zhou, Junjie Wang, Yanfei Li, Jiahang Wang, Zepeng Osamura, Kazuki Liu, Rujie Abe, Narishige Wang, Jingjie Zhang, Shunli Xie, Haojun Wu, Jiajun Wu, Weiming Kang, Wenxiong Gao, Qingshuo Xiong, Jiaming Ben, Xianye Chen, Lei Song, Lichen Cui, Junjian Xiong, Haijun Lu, Junhao Feng, Bin Liu, Mengyuan Zhou, Ji Zhao, Baoquan Xu, Ke Huang, Yongzhen Wang, Liang Marin-Jimenez, Manuel J Ahad, Md Atiqur Rahman Yu, Shiqi |
| contents | Human identification at a distance (HID) is challenging because traditional biometric modalities such as face and fingerprints are often difficult to acquire in real-world scenarios. Gait recognition provides a practical alternative, as it can be captured reliably at a distance. To promote progress in gait recognition and provide a fair evaluation platform, the International Competition on Human Identification at a Distance (HID) has been organized annually since 2020. Since 2023, the competition has adopted the challenging SUSTech-Competition dataset, which features substantial variations in clothing, carried objects, and view angles. No dedicated training data are provided, requiring participants to train their models using external datasets. Each year, the competition applies a different random seed to generate distinct evaluation splits, which reduces the risk of overfitting and supports a fair assessment of cross-domain generalization. While HID 2023 and HID 2024 already used this dataset, HID 2025 explicitly examined whether algorithmic advances could surpass the accuracy limits observed previously. Despite the heightened difficulty, participants achieved further improvements, and the best-performing method reached 94.2% accuracy, setting a new benchmark on this dataset. We also analyze key technical trends and outline potential directions for future research in gait recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_07565 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Human Identification at a Distance: Challenges, Methods and Results on the Competition HID 2025 Ma, Jingzhe Zhang, Meng Yu, Jianlong Liu, Kun Xu, Zunxiao Cheng, Xue Zhou, Junjie Wang, Yanfei Li, Jiahang Wang, Zepeng Osamura, Kazuki Liu, Rujie Abe, Narishige Wang, Jingjie Zhang, Shunli Xie, Haojun Wu, Jiajun Wu, Weiming Kang, Wenxiong Gao, Qingshuo Xiong, Jiaming Ben, Xianye Chen, Lei Song, Lichen Cui, Junjian Xiong, Haijun Lu, Junhao Feng, Bin Liu, Mengyuan Zhou, Ji Zhao, Baoquan Xu, Ke Huang, Yongzhen Wang, Liang Marin-Jimenez, Manuel J Ahad, Md Atiqur Rahman Yu, Shiqi Computer Vision and Pattern Recognition Human identification at a distance (HID) is challenging because traditional biometric modalities such as face and fingerprints are often difficult to acquire in real-world scenarios. Gait recognition provides a practical alternative, as it can be captured reliably at a distance. To promote progress in gait recognition and provide a fair evaluation platform, the International Competition on Human Identification at a Distance (HID) has been organized annually since 2020. Since 2023, the competition has adopted the challenging SUSTech-Competition dataset, which features substantial variations in clothing, carried objects, and view angles. No dedicated training data are provided, requiring participants to train their models using external datasets. Each year, the competition applies a different random seed to generate distinct evaluation splits, which reduces the risk of overfitting and supports a fair assessment of cross-domain generalization. While HID 2023 and HID 2024 already used this dataset, HID 2025 explicitly examined whether algorithmic advances could surpass the accuracy limits observed previously. Despite the heightened difficulty, participants achieved further improvements, and the best-performing method reached 94.2% accuracy, setting a new benchmark on this dataset. We also analyze key technical trends and outline potential directions for future research in gait recognition. |
| title | Human Identification at a Distance: Challenges, Methods and Results on the Competition HID 2025 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.07565 |