基于一套独特手指静脉识别算法的漏洞评估与表示攻击检测

Johannes Schuiki, Georg Wimmer, A. Uhl
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引用次数: 4

摘要

将伪造的生物特征样本呈现给生物特征捕捉装置的行为被称为呈现攻击。在过去的十年中,这种类型的攻击已经解决了各种生物特征,并且仍然是一个广泛研究的话题。这项研究遵循了先前发表的一项工作的想法,该工作采用了12种算法进行手指静脉识别,以便对演示攻击数据库进行广泛的漏洞分析。目前的工作采用了这一想法,并检查了两个已经存在的手指静脉呈现攻击数据库,目的是从更广泛的角度评估这些呈现攻击的危险性。此外,本研究表明,通过结合不同算法的匹配分数,可以实现表示攻击检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vulnerability Assessment and Presentation Attack Detection Using a Set of Distinct Finger Vein Recognition Algorithms
The act of presenting a forged biometric sample to a bio-metric capturing device is referred to as presentation at-tack. During the last decade this type of attack has been addressed for various biometric traits and is still a widely researched topic. This study follows the idea from a previously published work which employs the usage of twelve algorithms for finger vein recognition in order to perform an extensive vulnerability analysis on a presentation at-tack database. The present work adopts this idea and examines two already existing finger vein presentation attack databases with the goal to evaluate how hazardous these presentation attacks are from a wider perspective. Additionally, this study shows that by combining the matching scores from different algorithms, presentation attack detection can be achieved.
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