Finger-vein-based biometric recognition technology has recently attracted the attention of both academia and industry because of its robustness against presentation attacks and the convenience of the acquisition process. As a matter of fact, some contactless vein-based recognition systems have already been deployed and commercialized. However, they require the users to keep their hands still over the acquisition device for a few seconds to perform recognition. In this study, we release this constraint and allow users to have their finger vein patterns acquired on-the-fly. To accomplish this goal, we introduce an ad-hoc acquisition architecture capable of capturing the finger vein structure using an array of low-cost cameras, and we propose a recognition framework based on the use of convolutional and recurrent neural networks. To test the proposed approach we acquire a finger vein image dataset, in video format at four different exposure times, from 100 subjects. The obtained experimental results show that, even in a very challenging scenario, the proposed system guarantees high performance levels, up to 99.13% recognition accuracy over the collected dataset.

中文翻译：

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基于深度神经网络的即时基于手指静脉的生物特征识别

基于手指静脉的生物特征识别技术由于其对呈现攻击的鲁棒性和获取过程的便利性，最近引起了学术界和工业界的关注。事实上，一些基于非接触静脉的识别系统已经被部署和商业化。但是，它们要求用户将手放在采集设备上几秒钟，以进行识别。在这项研究中，我们释放了此约束，并允许用户即时获取其手指静脉模式。为了实现此目标，我们引入了一种临时捕获架构，该架构能够使用一系列低成本相机捕获指静脉结构，并提出一种基于卷积神经网络和递归神经网络的识别框架。为了测试所提出的方法，我们从100位受试者中获取了视频格式的手指静脉图像数据集，该数据集在四种不同的曝光时间下均以视频格式显示。获得的实验结果表明，即使在非常困难的情况下，所提出的系统也可以保证较高的性能水平，在所收集的数据集上的识别准确率高达99.13％。