In practical iris applications, the obtained iris images are inevitably affected by noises brought by uneven illumination, off-angle view, eyelids, and eyelashes etc. Such influences can lead to poor accuracy and low efficiency of iris localization. This paper presents a novel conformal geometric algebra (CGA) based algorithm for accurate and fast iris localization. Firstly, two thresholds are obtained adaptively to convert the gray image into three-value image and Sobel edge detector is used to generate the edge points. Then an improved CGA-based circle detection algorithm is applied to detect the limbic and pupillary boundaries of the iris. Candidate boundaries are found with a priori knowledge of the eye structure. Finally, the CGA inner product is used to decide whether an edge point from candidate boundaries is on the circle or not. A defect ratio is defined to represent the completeness of each candidate boundary, and only those with the lowest defect ratio are set as the pupillary and limbic boundaries. Our algorithm detects candidate boundaries that satisfy a priori constraints rapidly and ensures the accuracy of iris localization. Experimental results on different datasets demonstrate the effectiveness and robustness of the algorithm.

在实际的虹膜应用中，所获得的虹膜图像不可避免地受到照明不均匀，斜视角，眼睑和睫毛等带来的噪声的影响。这种影响会导致虹膜定位的准确性降低和效率降低。本文提出了一种新颖的基于共形几何代数（CGA）的算法，可以快速准确地进行虹膜定位。首先，自适应地获得两个阈值以将灰度图像转换为三值图像，然后使用Sobel边缘检测器生成边缘点。然后将一种改进的基于CGA的圆圈检测算法应用于检测虹膜的边缘和瞳孔边界。通过眼结构的先验知识可以找到候选边界。最后，使用CGA内积来确定来自候选边界的边缘点是否在圆上。定义缺陷率以表示每个候选边界的完整性，并且仅将那些缺陷率最低的缺陷率设置为瞳孔和边缘边界。我们的算法可以快速检测出满足先验约束的候选边界，并确保虹膜定位的准确性。在不同数据集上的实验结果证明了该算法的有效性和鲁棒性。