Kolda Lukas, Krejcar Ondrej, Selamat Ali, Kuca Kamil, Fadeyi Oluwaseun
Faculty of Informatics and Management, University of Hradec Kralove, Hradec Kralove 50003, Czech Republic.
Malaysia Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia Kuala Lumpur, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia.
Sensors (Basel). 2019 Aug 26;19(17):3709. doi: 10.3390/s19173709.
Biometric verification methods have gained significant popularity in recent times, which has brought about their extensive usage. In light of theoretical evidence surrounding the development of biometric verification, we proposed an experimental multi-biometric system for laboratory testing. First, the proposed system was designed such that it was able to identify and verify a user through the hand contour, and blood flow (blood stream) at the upper part of the hand. Next, we detailed the hard and software solutions for the system. A total of 40 subjects agreed to be a part of data generation team, which produced 280 hand images. The core of this paper lies in evaluating individual metrics, which are functions of frequency comparison of the double type faults with the EER (Equal Error Rate) values. The lowest value was measured for the case of the modified Hausdorff distance metric - Maximally Helicity Violating (MHV). Furthermore, for the verified biometric characteristics (Hamming distance and MHV), appropriate and suitable metrics have been proposed and experimented to optimize system precision. Thus, the EER value for the designed multi-biometric system in the context of this work was found to be 5%, which proves that metrics consolidation increases the precision of the multi-biometric system. Algorithms used for the proposed multi-biometric device shows that the individual metrics exhibit significant accuracy but perform better on consolidation, with a few shortcomings.
生物特征验证方法近年来广受欢迎，这导致了它们的广泛应用。鉴于围绕生物特征验证发展的理论证据，我们提出了一种用于实验室测试的实验性多生物特征系统。首先，所提出的系统被设计为能够通过手部轮廓以及手部上部的血流（血流情况）来识别和验证用户。接下来，我们详细介绍了该系统的硬件和软件解决方案。共有40名受试者同意成为数据生成团队的一部分，他们生成了280张手部图像。本文的核心在于评估各个指标，这些指标是双类型故障与等错误率（EER）值的频率比较函数。对于改进的豪斯多夫距离度量——最大螺旋度违反（MHV）的情况，测量到了最低值。此外，对于已验证的生物特征（汉明距离和MHV），已经提出并试验了合适且适用的指标以优化系统精度。因此，在这项工作的背景下，所设计的多生物特征系统的EER值为5%，这证明了指标整合提高了多生物特征系统的精度。用于所提出的多生物特征设备的算法表明，各个指标具有显著的准确性，但在整合时表现更好，不过也存在一些缺点。
