Despite claims that finger-vein biometrics can detect aliveness, recent research has shown that current systems can be fooled by forged vein patterns printed on a distinctive paper, raising considerable security concerns regarding the identification authenticity of these systems. Additionally, finger-vein identification exhibits low accuracy rates in real-world applications due to the inferior image quality caused by varied finger thicknesses and vein pattern variations caused by finger axial rotation. To address these issues, we propose a lightweight convolutional neural network (CNN) called the Finger-Vein Recognition and AntiSpoofing Network (FVRAS-Net), which integrates the recognition task and the antispoofing task into a unified CNN model by utilizing a multitask learning (MTL) approach and achieves high security and strong real-time performance. Then, a multi-intensity illumination strategy is introduced into the embedded biometric system to automatically select the most informative image for finger-vein identification, which can effectively improve the recognition performance of the real system. Finally, a challenging finger-vein database with images depicting severe axial finger rotation is built for more rigorous validation of the proposed system, which enriches the database resources for the finger-vein recognition community. Experiments demonstrate that the proposed FVRAS-Net achieves excellent performance in both recognition and antispoofing tasks on public data sets, especially on challenging databases with images depicting axial rotation.

中文翻译：

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FVRAS-Net：使用统一 CNN 的嵌入式手指静脉识别和反欺骗系统

尽管声称手指静脉生物识别技术可以检测活动性，但最近的研究表明，当前的系统可能会被印在独特纸张上的伪造静脉图案所欺骗，从而引发了对这些系统识别真实性的相当大的安全担忧。此外，由于手指厚度变化和手指轴向旋转引起的静脉图案变化导致图像质量较差，因此手指静脉识别在实际应用中的准确率较低。为了解决这些问题，我们提出了一种称为手指静脉识别和反欺骗网络 (FVRAS-Net) 的轻量级卷积神经网络 (CNN)，它利用多任务学习（MTL）方法将识别任务和反欺骗任务集成到一个统一的CNN模型中，实现了高安全性和强实时性。然后，在嵌入式生物识别系统中引入多强度照明策略，自动选择信息量最大的图像进行指静脉识别，有效提高真实系统的识别性能。最后，为了更严格地验证所提出的系统，构建了一个具有挑战性的手指静脉数据库，其中包含描绘严重轴向手指旋转的图像，这丰富了手指静脉识别社区的数据库资源。实验表明，所提出的 FVRAS-Net 在公共数据集的识别和反欺骗任务中均取得了优异的性能，