Obfuscating an iris recognition system through forged iris samples has been a major security threat in iris-based authentication. Therefore, a detection mechanism is essential that may explicitly discriminate between the live iris and forged (attack) patterns. The majority of existing methods analyze the eye image as a whole to find discriminatory features for fake and real iris. However, many attacks do not alter the entire eye image, instead merely the iris region is affected. It infers that the iris embodies the region of interest (RoI) for an exhaustive search towards identifying forged iris patterns. This paper introduces a novel framework that locates RoI using the YOLO approach and performs selective image enhancement to enrich the core textural details. The YOLO approach tightly bounds the iris region without any pattern loss, where the textural analysis through local and global descriptors is expected to be efficacious. Afterward, various handcrafted and CNN based methods are employed to extract the discriminative textural features from the RoI. Later, the best-k features are identified through the Friedman test as the optimal feature set and combined using score-level fusion. Further, the proposed approach is assessed on six different iris databases using predefined intra-dataset, cross-dataset, and combined-dataset validation protocols. The experimental outcomes exhibit that the proposed method results in significant error reduction with the state of the arts.

通过伪造的虹膜样本混淆虹膜识别系统已成为基于虹膜的身份验证的主要安全威胁。因此，检测机制是必不可少的，可以明确区分实时虹膜和伪造（攻击）模式。现有的大多数方法都会对整个眼睛图像进行分析，以发现假虹膜和真实虹膜的区别特征。但是，许多攻击并不会改变整个眼睛的图像，而是仅影响虹膜区域。它推断虹膜体现了感兴趣的区域（RoI），以进行详尽的搜索以识别伪造的虹膜图案。本文介绍了一种新颖的框架，该框架使用YOLO方法定位RoI，并执行选择性图像增强以丰富核心纹理细节。YOLO方法紧紧围绕虹膜区域，没有任何图案损失，预计通过局部和全局描述符进行的纹理分析将是有效的。之后，采用各种基于CNN的手工方法从RoI中提取出具有区别性的纹理特征。后来，最好的通过Friedman检验将k个特征识别为最佳特征集，并使用得分级融合对其进行组合。此外，使用预定义的内部数据集，跨数据集和组合数据集验证协议在六个不同的虹膜数据库上评估了所提出的方法。实验结果表明，所提出的方法可以大大降低现有技术的误差。