Iris segmentation plays a critical role in the iris biometric systems. It has two modules: iris localization and noise detection. The first module demarcates the actual iris’ inner and outer boundaries in input eyeimages. The second module detects and removes noise in the valid iris part. Researchers devised numerous iris segmentation and/or localization schemes, which are based on the histogram and thresholding, circular Hough transform (CHT), Integro-differential operator (IDO), active contour models, graph-cuts, or deep learning. It is observed that most contemporary schemes perform poorly when confronted with images containing noisy factors such as the eyebrows, eyelashes, contact lenses, non-uniform illumination, light reflections, defocus and/or eyeglasses. The performance of CHT and IDO against noise is found robust, but these operators are computationally expensive. On the other hand, the histogram and thresholding-based schemes are considered fast, but these are less robust against noise. Besides, most contemporary schemes mark iris contours with a circle approximation and offer no noise removal strategy. To address these issues, this study offers an effective iris segmentation algorithm. First, it applies an optimized coarse-to-fine scheme based on an adaptive threshold to mark iris inner boundary. Next, it detects and marks eyelashes adaptively. After that, it marks iris outer boundary via an optimized coarse-to-fine scheme. Then, it regularizes the non-circular iris’ contours using the Fourier series. Finally, eyelids and reflections are marked in the iris polar form. The proposed scheme shows better results on the CASIA-Iris-Interval V3.0, IITD V1.0, and MMU V1.0 iris databases.

虹膜分割在虹膜生物识别系统中起着至关重要的作用。它具有两个模块：虹膜定位和噪声检测。第一个模块在输入眼影中划定实际虹膜的内部和外部边界。第二个模块检测并消除有效虹膜部分中的噪声。研究人员设计了多种虹膜分割和/或定位方案，这些方案基于直方图和阈值，圆形霍夫变换（CHT），积分微分算子（IDO），活动轮廓模型，图形切割或深度学习。可以观察到，大多数现代方案在遇到包含噪声因素（例如眉毛，睫毛，隐形眼镜，照明不均匀，反射光，散焦和/或眼镜）的图像时效果较差。发现CHT和IDO的抗噪声性能很强，但是这些运算符的计算量很大。另一方面，直方图和基于阈值的方案被认为是快速的，但是它们对噪声的鲁棒性较差。此外，大多数现代方案都将虹膜轮廓标记为圆形近似值，并且不提供除噪策略。为了解决这些问题，本研究提供了一种有效的虹膜分割算法。首先，它基于自适应阈值应用优化的从粗到精方案，以标记虹膜内部边界。接下来，它自适应地检测并标记睫毛。之后，它通过优化的从粗到精方案标记虹膜的外部边界。然后，使用傅立叶级数对非圆形虹膜轮廓进行正则化。最后，眼睑和反射以虹膜极性标记。拟议的方案在CASIA-Iris-Interval V3.0，IITD V1.0，