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.

翻译：基于个体行走时足底独特压力模式的生物特征步态识别，是一个在安防与安全领域应用日益增长的新兴研究方向。然而，该领域的发展一直受限于缺乏大规模、多样化的数据集，而这些数据对于解决关键挑战（如对新用户的泛化能力以及对鞋具或步行速度等因素变化的鲁棒性）至关重要。近期发布的UNB StepUP-P150数据集——迄今为止规模最大、最全面的高分辨率步态压力记录集合——为通过深度学习应对这些挑战提供了新的机遇。为纪念这一里程碑，首届国际步态生物识别StepUP竞赛应运而生。参赛者的任务是利用StepUP-P150数据集开发鲁棒的识别模型，随后在一个独立的专用测试集上进行评估；该测试集旨在给定有限且相对同质的参考数据条件下，评估模型在具有挑战性的变异场景下的验证性能。本次竞赛吸引了全球范围的参与，共有来自学术界和工业界的23支团队注册。表现最佳的团队Saeid_UCC采用生成式奖励机（GRM）优化策略，取得了10.77%的最佳等错误率（EER）。总体而言，竞赛展示了强有力的解决方案，但对陌生鞋具的泛化能力仍存在持续挑战，这凸显了未来工作的一个关键方向。