Iris images captured in non-cooperative environments often suffer from adverse noise, which challenges many existing iris segmentation methods. To address this problem, this paper proposes a high-efficiency deep learning based iris segmentation approach, named IrisParseNet. Different from many previous CNN-based iris segmentation methods, which only focus on predicting accurate iris masks by following popular semantic segmentation frameworks, the proposed approach is a complete iris segmentation solution, i.e., iris mask and parameterized inner and outer iris boundaries are jointly achieved by actively modeling them into a unified multi-task network. Moreover, an elaborately designed attention module is incorporated into it to improve the segmentation performance. To train and evaluate the proposed approach, we manually label three representative and challenging iris databases, i.e., CASIA.v4-distance, UBIRIS.v2, and MICHE-I, which involve multiple illumination (NIR, VIS) and imaging sensors (long-range and mobile iris cameras), along with various types of noises. Additionally, several unified evaluation protocols are built for fair comparisons. Extensive experiments are conducted on these newly annotated databases, and results show that the proposed approach achieves state-of-the-art performance on various benchmarks. Further, as a general drop-in replacement, the proposed iris segmentation method can be used for any iris recognition methodology, and would significantly improve the performance of non-cooperative iris recognition.

中文翻译：

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使用深度多任务注意网络进行非合作虹膜识别，实现完整且准确的虹膜分割


在非合作环境中捕获的虹膜图像经常受到不利噪声的影响，这对许多现有的虹膜分割方法提出了挑战。为了解决这个问题，本文提出了一种基于深度学习的高效虹膜分割方法，命名为 IrisParseNet。与之前许多基于CNN的虹膜分割方法只关注通过遵循流行的语义分割框架来预测准确的虹膜掩模不同，该方法是一个完整的虹膜分割解决方案，即虹膜掩模和参数化的虹膜内外边界共同实现通过积极地将它们建模为统一的多任务网络。此外，它还融入了精心设计的注意力模块，以提高分割性能。为了训练和评估所提出的方法，我们手动标记了三个具有代表性和挑战性的虹膜数据库，即 CASIA.v4-distance、UBIRIS.v2 和 MICHE-I，其中涉及多个照明（NIR、VIS）和成像传感器（长光）范围和移动虹膜相机），以及各种类型的噪音。此外，还建立了几个统一的评估协议以进行公平比较。对这些新注释的数据库进行了广泛的实验，结果表明所提出的方法在各种基准上实现了最先进的性能。此外，作为一般的直接替代，所提出的虹膜分割方法可以用于任何虹膜识别方法，并且将显着提高非合作虹膜识别的性能。