Abstract
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.
Abstract (translated)
远距离人类识别（HID）具有挑战性，因为传统的生物特征模态如面部和指纹在现实场景中往往难以采集。步态识别提供了一个实用的替代方案，因为它可以在较远的距离可靠地捕捉到个体的行走方式。为了促进步态识别的进步并为该领域提供一个公平的评估平台，国际远距离人类识别竞赛（HID）自2020年起每年举办一次。从2023年开始，比赛采用了具有挑战性的SUSTech-Competition数据集，该数据集包含了服装、携带物品和视角角度方面的显著变化。由于没有提供专门用于训练的数据集，参赛者必须使用外部数据集来训练他们的模型。竞赛每年应用不同的随机种子生成不同的评估分组，这减少了过拟合的风险，并支持跨域泛化的公平评价。尽管HID 2023和HID 2024已经采用了这个数据集，但HID 2025明确地检查了算法进步是否可以超过先前观察到的准确性限制。即使难度增加，参赛者仍然取得了进一步的进步，最优秀的方法达到了94.2%的准确率，在该数据集上设立了新的基准。我们还分析了关键技术趋势，并概述了步态识别未来研究的潜在方向。
URL
https://arxiv.org/abs/2602.07565
