Human Identification at a Distance: Challenges, Methods and Results on the Competition HID 2025

TL;DR

This paper discusses the challenges of human identification at a distance, focusing on gait recognition, and reports on the HID 2025 competition which achieved a new accuracy benchmark of 94.2% using a challenging dataset.

Contribution

It introduces the HID 2025 competition, evaluates recent algorithmic advances, and establishes a new accuracy benchmark on a difficult gait recognition dataset.

Findings

Participants achieved 94.2% accuracy, surpassing previous results.

The competition used a challenging dataset with variations in clothing and view angles.

Analysis of technical trends suggests future research directions.

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.