The 2nd International StepUP Competition for Biometric Footstep Recognition: From Steps to Strides

• URL: http://arxiv.org/abs/2607.13905v1
• Date: Wed, 15 Jul 2026 14:46:37 GMT
• Title: The 2nd International StepUP Competition for Biometric Footstep Recognition: From Steps to Strides
• Abstract summary: The International StepUP Competition Series was launched to advance research in pressure-based footstep biometrics.<n>The 2nd edition of the competition addressed three key challenges: (1) to distract users with limited enrollment data, (2) to domain variations caused by robustness in footwear and walking speed, and (3) effective fusion of paired left-right footsteps.
• Score: 2.947038688451675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The International StepUP Competition Series was launched to advance research in pressure-based footstep biometrics through a standardized and challenging evaluation framework. Using the large-scale StepUP-P150 dataset (with more than 200,000 high-resolution dynamic footsteps from 150 individuals) and a previously unreleased test set, the 2nd edition of the competition addressed three key challenges: (1) generalization to unseen users with limited enrollment data, (2) robustness to domain shift caused by variations in footwear and walking speed and (3) effective fusion of paired left-right footsteps. While the first two challenges built on the inaugural competition, this edition introduced more extreme cross-domain conditions and moved beyond isolated footsteps to stride-level verification, enabling new opportunities for representation learning and inter-step information fusion. The competition attracted 26 registrants from academia and industry, with a best equal error rate of 8.00% achieved by the ArogyaPandit Research Team using a spatiotemporal CNN combined with an ensemble-based scoring strategy. The top solutions showcase the value of harnessing temporal patterns and of incorporating inference-time normalization and calibration strategies to improve scoring. However, the results also reveal that recognizing users in unseen personal footwear remains a challenge, especially in the presence of distractors with similar characteristics.

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