Biometric verification systems are deployed in various security-based access-control applications that require user-friendly and reliable person verification. Among the different biometric characteristics, fingervein biometrics have been extensively studied owing to their reliable verification performance. Furthermore, fingervein patterns reside inside the skin and are not visible outside; therefore, they possess inherent resistance to presentation attacks and degradation due to external factors. In this paper, we introduce a novel fingervein verification technique using a convolutional multihead attention network called VeinAtnNet. The proposed VeinAtnNet is designed to achieve light weight with a smaller number of learnable parameters while extracting discriminant information from both normal and enhanced fingervein images. The proposed VeinAtnNet was trained on the newly constructed fingervein dataset with 300 unique fingervein patterns that were captured in multiple sessions to obtain 92 samples per unique fingervein. Extensive experiments were performed on the newly collected dataset FV-300 and the publicly available FV-USM and FV-PolyU fingervein dataset. The performance of the proposed method was compared with five state-of-the-art fingervein verification systems, indicating the efficacy of the proposed VeinAtnNet.

翻译：生物特征验证系统部署于各类需要便捷可靠人员验证的安全访问控制应用中。在众多生物特征中，指静脉因其可靠的验证性能而受到广泛研究。此外，指静脉纹路位于皮肤内部且不可见，故对呈现攻击和外部因素造成的退化具有天然抗性。本文提出一种名为VeinAtnNet的新型指静脉验证技术，该技术采用卷积多头注意力网络。所提出的VeinAtnNet旨在通过更少的可学习参数实现轻量化设计，同时从正常和增强指静脉图像中提取判别性信息。该网络使用新构建的包含300种独特指静脉纹路的数据集进行训练，每种纹路在多轮采集中获得92个样本。我们在新采集的FV-300数据集以及公开的FV-USM和FV-PolyU指静脉数据集上开展了大量实验。将所提方法与五种最先进的指静脉验证系统进行性能对比，结果表明VeinAtnNet具有显著有效性。