2015虹膜活体检测大赛

David Yambay, Brian Walczak, S. Schuckers, A. Czajka
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引用次数: 47

摘要

展示攻击,如打印虹膜图像或图案隐形眼镜可以用来绕过虹膜识别系统。已经提出了不同的解决方案来抵消这个漏洞,使用表示攻击检测(通常称为活动性检测)来检测攻击的存在,但是独立的评估和比较很少。为了填补这一空白,我们在2013年推出了第一届国际虹膜生活大赛。本文介绍了2015年组织的第二版(LivDet-Iris 2015)的详细结果。提出了四种基于软件的表示攻击检测方法。使用标准化测试协议和大量实时和伪造虹膜图像,对三种不同的虹膜数据集进行了结果统计。采用Federico算法,活样本的拒绝率为1.68%,欺骗样本的接受率为5.48%,效果最好。这表明,基于纸质打印输出和打印隐形眼镜的简单静态攻击仍然很难被纯粹基于软件的方法识别。与2013年的版本类似,与图案隐形眼镜相比,打印的虹膜图像更容易与实时图像区分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LivDet-Iris 2015 - Iris Liveness Detection Competition 2015
Presentation attacks such as printed iris images or patterned contact lenses can be used to circumvent an iris recognition system. Different solutions have been proposed to counteract this vulnerability with Presentation Attack Detection (commonly called liveness detection) being used to detect the presence of an attack, yet independent evaluations and comparisons are rare. To fill this gap we have launched the first international iris liveness competition in 2013. This paper presents detailed results of its second edition, organized in 2015 (LivDet-Iris 2015). Four software-based approaches to Presentation Attack Detection were submitted. Results were tallied across three different iris datasets using a standardized testing protocol and large quantities of live and spoof iris images. The Federico Algorithm received the best results with a rate of rejected live samples of 1.68% and rate of accepted spoof samples of 5.48%. This shows that simple static attacks based on paper printouts and printed contact lenses are still challenging to be recognized purely by software-based approaches. Similar to the 2013 edition, printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses.
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