Iris localization is the crucial link of iris recognition and automatic eye tracking. Based on the traditional Hough transform, this paper proposes an accurate pupil detection method combined with ellipse fitting and circular data compensation. We used the minimum gray mean method to approximately determine the inner edge. According to the results, the inner edge image is extracted and finely located by the center compensation method. When locating the outer boundary, a coarse localization is performed based on approximate radius compensation. The fine localization of the outer edge is performed based on approximate circle center compensation. The experimental results show the proposed algorithm improves the accuracy and real-time performance of the localization compared with the traditional method. It retains the original advantages of Hough transform while reduces the amount of computation and useless information.

中文翻译：

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基于Hough变换和二次圆数据补偿的虹膜边界定位

虹膜定位是虹膜识别和自动眼动追踪的关键环节。本文在传统霍夫变换的基础上，提出了一种结合椭圆拟合和圆形数据补偿的精确瞳孔检测方法。我们使用最小灰度平均法来近似确定内边缘。根据结果​​，通过中心补偿方法提取和精细定位内边缘图像。在定位外边界时，基于近似半径补偿进行粗定位。基于近似圆心补偿进行外边缘的精细定位。实验结果表明，与传统方法相比，该算法提高了定位精度和实时性。