论文标题
虹膜表现攻击检测的微观条纹分析
Micro Stripes Analyses for Iris Presentation Attack Detection
论文作者
论文摘要
虹膜识别系统容易受到演示攻击的影响，例如纹理隐形眼镜或印刷图像。在本文中，我们提出了一个轻巧的框架，以通过提取扩展的归一化虹膜纹理的多个微片段来检测虹膜表现攻击。在此过程中，修改了标准的虹膜分割。对于我们的演示攻击检测网络，可以更好地对分类问题进行建模，因此处理分段区域以提供较低的尺寸输入段和较高数量的学习样本。我们提出的微型条纹分析（MSA）解决方案将分段区域作为单个条纹进行样品。然后，大多数投票做出了这些微观条纹的最终分类决定。在五个数据库中证明了实验，其中两个数据库（IIITD-WVU和Notre Dame）来自Livdet-2017 Iris竞争。对该框架的深入实验评估表明，与最先进的算法相比，表现出色。此外，我们的解决方案最大程度地减少了纹理（攻击）和软（善意）隐形眼镜演示之间的混淆。
Iris recognition systems are vulnerable to the presentation attacks, such as textured contact lenses or printed images. In this paper, we propose a lightweight framework to detect iris presentation attacks by extracting multiple micro-stripes of expanded normalized iris textures. In this procedure, a standard iris segmentation is modified. For our presentation attack detection network to better model the classification problem, the segmented area is processed to provide lower dimensional input segments and a higher number of learning samples. Our proposed Micro Stripes Analyses (MSA) solution samples the segmented areas as individual stripes. Then, the majority vote makes the final classification decision of those micro-stripes. Experiments are demonstrated on five databases, where two databases (IIITD-WVU and Notre Dame) are from the LivDet-2017 Iris competition. An in-depth experimental evaluation of this framework reveals a superior performance compared with state-of-the-art algorithms. Moreover, our solution minimizes the confusion between textured (attack) and soft (bona fide) contact lens presentations.
