In the past two decads, iris spoofing detection has occupied an ample space in the literature of iris biometics. The textured lens may be used to spoof the Iris Recognition (IR) system by exploiting its external texture. Besides, the soft lens may cause an upsurge in the false rejection rate as it blurs the iris texture. Therefore, it is foremost to identify contact lens in human eyes before accessing an IR system. This paper proposes a novel fusion-based approach to discriminate live iris from contact lens images that combines handcrafted and data-driven features. It also demonstrates a Densely-connected Contact-lens Classification Network (DCCNet) as a data-driven model that is basically a customized Densenet121 framework. The DCCNet features are -pooled with handcrafted counterparts to create a combined feature set. However, the optimal features are identified by top-k feature selection using the Friedman test and are fused through score-level fusion. The assessment of the proposed approach includes several experiments simulated on three iris databases, i.e. Notre Dame (ND) Contact Lens 2013, IIIT-Delhi Contact Lens (IIITD), and Clarkson Databases. The equal error rate (EER) and the detection error tradeoff (DET) curve are used as performance metrics. Further, the statistical analysis is performed using Nemenyi and Bonferroni-Dunn tests, where the proposed approach significantly improves the state of the arts.

在过去的两个十年中，虹膜欺骗检测在虹膜生物仿制品的文献中占据了足够的空间。通过利用其外部纹理，可以将带纹理的镜头用来欺骗虹膜识别（IR）系统。此外，软镜头可能会导致假剔除率升高，因为它会使虹膜纹理模糊。因此，最重要的是在访问红外系统之前，先识别人眼中的隐形眼镜。本文提出了一种新颖的基于融合的方法，该方法将手工制作的和数据驱动的功能相结合，从而将活虹膜与隐形眼镜图像区分开。它还演示了作为数据驱动模型的密集连接的隐形眼镜分类网络（DCCNet），该模型基本上是定制的Densenet121框架。DCCNet功能与手工制作的对应功能合并在一起，以创建组合的功能集。然而，最佳特征通过使用Friedman检验的top-k特征选择进行识别，并通过分数级融合进行融合。对提出的方法的评估包括在三个虹膜数据库上模拟的几个实验，我。e。巴黎圣母院（ND）隐形眼镜2013，IIIT-德里隐形眼镜（IIITD）和克拉克森数据库。等错误率（EER）和检测错误权衡（DET）曲线用作性能指标。此外，使用Nemenyi和Bonferroni-Dunn检验执行统计分析，其中所提出的方法显着改善了现有技术。