First International StepUP Competition for Biometric Footstep Recognition: Methods, Results and Remaining Challenges

2026-02-11

AI Summary

This study addresses the challenge of generalizing plantar pressure–based gait biometrics under real-world variations such as unseen users, diverse footwear, and differing walking speeds. Leveraging the large-scale, high-resolution UNB StepUP-P150 dataset, the work presents the first systematic evaluation of model robustness in authentic scenarios through an international competition. Focusing on identity verification with limited and homogeneous reference data, the top-performing approach employed a Generative Reward Machine (GRM)–based optimization strategy. Among 23 participating teams worldwide, the best-performing model achieved an Equal Error Rate (EER) of 10.77%, confirming the viability of current methodologies while underscoring cross-footwear generalization as a critical bottleneck—an insight that charts a clear direction for future research.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Biometric footstep recognition, based on a person's unique pressure patterns under their feet during walking, is an emerging field with growing applications in security and safety. However, progress in this area has been limited by the lack of large, diverse datasets necessary to address critical challenges such as generalization to new users and robustness to shifts in factors like footwear or walking speed. The recent release of the UNB StepUP-P150 dataset, the largest and most comprehensive collection of high-resolution footstep pressure recordings to date, opens new opportunities for addressing these challenges through deep learning. To mark this milestone, the First International StepUP Competition for Biometric Footstep Recognition was launched. Competitors were tasked with developing robust recognition models using the StepUP-P150 dataset that were then evaluated on a separate, dedicated test set designed to assess verification performance under challenging variations, given limited and relatively homogeneous reference data. The competition attracted global participation, with 23 registered teams from academia and industry. The top-performing team, Saeid_UCC, achieved the best equal error rate (EER) of 10.77% using a generative reward machine (GRM) optimization strategy. Overall, the competition showcased strong solutions, but persistent challenges in generalizing to unfamiliar footwear highlight a critical area for future work.

Problem

• biometric footstep recognition
• generalization
• robustness
• footwear variation
• verification

Innovation

• biometric footstep recognition
• StepUP-P150 dataset
• generative reward machine
• robust verification
• cross-condition generalization