OpenVeinNet：结合动态蛇形卷积与图学习的鲁棒开放集指静脉验证方法
OpenVeinNet: Robust Open-Set Finger Vein Verification with Dynamic Snake Convolution and Graph Learning
- Norwegian University of Science and Technology (NTNU)（挪威科技大学）
- SAFE Centre（SAFE中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
OpenVeinNet结合动态蛇形卷积与图学习，引入质心角度混合损失，在5个公开指静脉数据集上实现了出色的开放集指静脉验证性能与跨数据集泛化能力。
AI中文摘要：
指静脉验证是一种有前景的生物特征认证模态，因为血管图案位于体内，难以从外部观察，且相对不易受呈现攻击影响。然而，在开放集场景中，可靠的验证仍具挑战性——训练时未见过测试身份，推理时必须拒绝未注册的探测样本。本文提出OpenVeinNet，一种专为跨数据集和开放集评估设计的指静脉验证框架。该模型将动态蛇形卷积与基于图的特征建模相结合：动态蛇形卷积通过自适应采样提取局部曲线状、管状的静脉结构，图卷积主干网络则建模静脉区域间的长程拓扑关系。为提升嵌入空间的判别性，本文引入质心角度混合损失函数，共同促进基于余弦相似度验证的类内紧凑性与类间角度分离。实验在5个公开指静脉数据集（FV-300、MMCBNU、FV-USM、PolyU、VERA）上开展，采用留一数据集训练方式，在基于注册的未知样本拒绝和全主体验证两种协议下进行评估，并与手工设计特征及近期基于深度学习的基线方法对比。结果显示，OpenVeinNet具备出色的跨数据集泛化能力，始终保持较低的等错误率，在固定误接受率操作点下也具有竞争力的真接受率。 ablation研究进一步证实了自适应管状特征提取、基于图的关系建模及所提损失函数各自及组合的贡献。这些发现表明，显式建模局部静脉几何、全局血管关系及角度紧凑嵌入对开放集指静脉验证是有效的。
英文摘要：
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.
