Department of Measurement and Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Krakow, Poland.
Sensors (Basel). 2020 Oct 6;20(19):5695. doi: 10.3390/s20195695.
Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to enhance the security of these devices by adding supplementary modality while keeping the user experience at the same level. Palm vein systems are based on infrared wavelengths used for capturing images of users' veins. It is both convenient for the user, and it is one of the most secure biometric solutions. The proposed system uses IR and UV wavelengths; the images are then processed by a deep convolutional neural network for extraction of biometric features and authentication of users. We tested the system in a verification scenario that consisted of checking if the images collected from the user contained the same biometric features as those in the database. The True Positive Rate (TPR) achieved by the system when the information from the two modalities were combined was 99.5% by the threshold of acceptance set to the Equal Error Rate (EER).
生物识别因素保护的设备和系统已成为我们生活的一部分，因为它们方便、易用、可靠且安全。这些设备利用我们身体独特特征的信息来验证用户身份。通过添加补充模式，可以提高这些设备的安全性，同时保持相同的用户体验。手掌静脉系统基于用于捕获用户静脉图像的红外波长。它既方便用户，也是最安全的生物识别解决方案之一。所提出的系统使用 IR 和 UV 波长；然后，通过深度卷积神经网络处理图像，以提取生物识别特征并验证用户。我们在验证场景中测试了该系统，该场景包括检查从用户收集的图像是否包含与数据库中相同的生物识别特征。当将两个模式的信息结合在一起，并将接受阈值设置为等错误率 (EER) 时，系统的真阳性率 (TPR) 达到 99.5%。
