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

2026-02-07

AI Summary

In long-range human identification, conventional biometrics such as faces and fingerprints are often inaccessible, making gait a critical alternative modality. This work addresses the HID 2025 international challenge under stringent conditions—no dedicated training data and cross-domain generalization—using the SUSTech-Competition dataset. To enable rigorous and fair evaluation, we introduce random seed–based generation of diverse test splits, systematically assessing whether existing algorithms can surpass current accuracy ceilings. Our approach integrates external-data pretraining, multi-view modeling, and occlusion-robust design, achieving a state-of-the-art identification accuracy of 94.2% on this dataset under complex real-world scenarios.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Humans and AI: AI for Accessibility
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Human Identification at a Distance
• Gait Recognition
• Biometric Modalities
• Cross-domain Generalization
• SUSTech-Competition Dataset

Innovation

• gait recognition
• cross-domain generalization
• SUSTech-Competition dataset
• human identification at a distance
• benchmark evaluation