Open-Set Vein Biometric Recognition with Deep Metric Learning
Pawe{\l} Pilarek, Marcel Musia{\l}ek, Anna G\'orska

TL;DR
This paper introduces a deep metric learning framework for open-set vein biometric recognition, enabling scalable, accurate identification and rejection of unseen subjects across diverse datasets.
Contribution
It proposes a prototype-based matching approach with calibrated similarity thresholds for open-set vein recognition, evaluated on multiple datasets with state-of-the-art results.
Findings
Achieves 99.6% Rank-1 accuracy on MMCBNU 6000 dataset.
Maintains high recognition performance with an OSCR of 0.9945.
Demonstrates robustness across different datasets and acquisition setups.
Abstract
Most state-of-the-art vein recognition methods rely on closed-set classification, which inherently limits their scalability and prevents the adaptive enrollment of new users without complete model retraining. We rigorously evaluate the computational boundaries of Deep Metric Learning (DML) under strict open-set constraints. Unlike standard closed-set approaches, we analyze the impact of data scarcity and domain shift on recognition performance. Our approach learns discriminative L2-normalised embeddings and employs prototype-based matching with a calibrated similarity threshold to effectively distinguish between enrolled users and unseen impostors. We evaluate the framework under a strict subject-disjoint protocol across four diverse datasets covering finger, wrist, and dorsal hand veins (MMCBNU 6000, UTFVP, FYO, and a dorsal hand-vein dataset). On the large-scale MMCBNU 6000 benchmark,…
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