Open-Set Vein Biometric Recognition with Deep Metric Learning
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...
| Authors | Paweł Pilarek, Marcel Musiałek, Anna Górska |
|---|---|
| Primary category | cs.CV |
| Published | 16 April 2026 |
| Last updated at source | 3 July 2026 |
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