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.

翻译：远距离人体身份识别（HID）具有挑战性，因为在现实场景中，人脸、指纹等传统生物特征模态往往难以有效采集。步态识别提供了一种实用的替代方案，因其可在远距离下被可靠地捕捉。为促进步态识别领域的进展并提供公平的评估平台，国际远距离人体身份识别竞赛（HID）自2020年起每年举办。自2023年起，该竞赛采用了具有挑战性的SUSTech-Competition数据集，该数据集在着装、携带物品和视角方面存在显著变化。竞赛不提供专用训练数据，要求参赛者使用外部数据集训练其模型。每年，竞赛通过不同的随机种子生成不同的评估划分，这降低了过拟合风险，并支持对跨域泛化能力的公平评估。尽管HID 2023和HID 2024已使用该数据集，但HID 2025明确探讨了算法进展能否超越先前观察到的精度极限。尽管难度增加，参赛者仍取得了进一步的提升，最佳方法的准确率达到94.2%，为该数据集设立了新的基准。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。