LivDet iris 2017 — Iris liveness detection competition 2017

David Yambay, Benedict Becker, Naman Kohli, Daksha Yadav, A. Czajka, K. Bowyer, S. Schuckers, Richa Singh, Mayank Vatsa, A. Noore, Diego Gragnaniello, Carlo Sansone, L. Verdoliva, Lingxiao He, Yiwei Ru, Haiqing Li, Nianfeng Liu, Zhenan Sun, T. Tan
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引用次数: 80

Abstract

Presentation attacks such as using a contact lens with a printed pattern or printouts of an iris can be utilized to bypass a biometric security system. The first international iris liveness competition was launched in 2013 in order to assess the performance of presentation attack detection (PAD) algorithms, with a second competition in 2015. This paper presents results of the third competition, LivDet-Iris 2017. Three software-based approaches to Presentation Attack Detection were submitted. Four datasets of live and spoof images were tested with an additional cross-sensor test. New datasets and novel situations of data have resulted in this competition being of a higher difficulty than previous competitions. Anonymous received the best results with a rate of rejected live samples of 3.36% and rate of accepted spoof samples of 14.71%. The results show that even with advances, printed iris attacks as well as patterned contacts lenses are still difficult for software-based systems to detect. Printed iris images were easier to be differentiated from live images in comparison to patterned contact lenses as was also seen in previous competitions.
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LivDet虹膜2017 -虹膜活性检测大赛2017
展示攻击,例如使用带有打印图案的隐形眼镜或虹膜的打印输出,可以用来绕过生物识别安全系统。首届国际虹膜活性竞赛于2013年启动,旨在评估呈现攻击检测(PAD)算法的性能,第二届竞赛于2015年举行。本文介绍了第三届竞赛livet - iris 2017的结果。提出了三种基于软件的表示攻击检测方法。通过额外的交叉传感器测试测试了四个实时和欺骗图像数据集。新的数据集和新的数据情况导致本次比赛比以往的比赛具有更高的难度。匿名的效果最好,活体样本的拒绝率为3.36%,欺骗样本的接受率为14.71%。结果表明,即使有了进步,打印虹膜攻击和图案隐形眼镜仍然很难被基于软件的系统检测到。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开来,这在之前的比赛中也可以看到。
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