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This paper introduces OpenVeinNet, a novel framework for finger vein verification that effectively addresses the challenges of open-set scenarios by utilizing Dynamic Snake Convolution and graph-based feature modeling. The model enhances local curvilinear vein structure extraction and captures long-range topological relationships, significantly improving the discriminative quality of the embedding space through a Centroid Angular Hybrid Loss. Experimental results across five public datasets demonstrate OpenVeinNet's superior cross-dataset generalization and competitive performance in both enrolment-based unknown rejection and full-subject verification protocols.
OpenVeinNet achieves unprecedented cross-dataset generalization in finger vein verification by combining advanced feature extraction with graph learning techniques.
Finger vein verification is a promising biometric modality for secure authentication because vascular patterns are internal, difficult to observe externally, and relatively resistant to presentation attacks. However, reliable verification remains challenging in open-set settings, where test identities are unseen during training and non-enrolled probes must be rejected at inference. This paper presents OpenVeinNet, a finger vein verification framework designed for cross-dataset and open-set evaluation. The proposed model combines Dynamic Snake Convolution with graph-based feature modelling. Dynamic Snake Convolution extracts local curvilinear and tubular vein structures using adaptive sampling, while the graph convolutional backbone models long-range topological relationships between vein regions. To improve the discriminative quality of the embedding space, we introduce a Centroid Angular Hybrid Loss, which jointly encourages intra-class compactness and inter-class angular separation for cosinesimilaritybased verification. Experiments are conducted on five public finger vein datasets: FV-300, MMCBNU, FV-USM, PolyU, and VERA. The method is evaluated using leaveonedatasetout training under both enrolmentbased unknownrejection and fullsubject verification protocols, and is compared with handcrafted and recent deep learning-based baselines. The results show that OpenVeinNet achieves strong cross-dataset generalisation, consistently low equal error rates, and competitive true accept rates at fixed false accept rate operating points. Ablation studies further confirm the individual and combined contributions of adaptive tubular feature extraction, graph-based relational modelling, and the proposed loss function. These findings indicate that explicitly modelling local vein geometry, global vascular relationships, and angularly compact embeddings is effective for openset finger vein verification.
