论文标题
学生中心检测方法：比较分析
Pupil Center Detection Approaches: A comparative analysis
论文作者
论文摘要
在过去的十年中，开发进行眼动追踪的技术和工具一直是不断增长的领域。使用图像处理技术检测学生的中心一直是此过程中的重要步骤。已经提出了大量的技术用于使用传统图像处理和基于机器学习的方法进行学生中心检测。尽管提出了大量方法，但使用相同的图像和性能指标，未发现其性能的比较工作。在这项工作中，我们旨在比较四种最常被引用的传统方法，用于瞳孔中心检测，以准确性，鲁棒性和计算成本进行比较。这些方法基于圆形的霍夫变换，椭圆拟合，Daugman的Integro-Differential Operator和径向对称性变换。比较分析使用来自CASIA-IRISV3和CASIA-IRISV4数据库的800个红外图像，其中包含各种类型的干扰。最佳性能是通过基于径向对称变换的方法获得的，其准确性和平均鲁棒性高于94％。用椭圆拟合方法获得的最短处理时间为0.06 s。
In the last decade, the development of technologies and tools for eye tracking has been a constantly growing area. Detecting the center of the pupil, using image processing techniques, has been an essential step in this process. A large number of techniques have been proposed for pupil center detection using both traditional image processing and machine learning-based methods. Despite the large number of methods proposed, no comparative work on their performance was found, using the same images and performance metrics. In this work, we aim at comparing four of the most frequently cited traditional methods for pupil center detection in terms of accuracy, robustness, and computational cost. These methods are based on the circular Hough transform, ellipse fitting, Daugman's integro-differential operator and radial symmetry transform. The comparative analysis was performed with 800 infrared images from the CASIA-IrisV3 and CASIA-IrisV4 databases containing various types of disturbances. The best performance was obtained by the method based on the radial symmetry transform with an accuracy and average robustness higher than 94%. The shortest processing time, obtained with the ellipse fitting method, was 0.06 s.
