Pupil parameters are the essential foundation for many applications, such as cognitive science and human–machine interaction. Existing approaches are still affected by various challenges. We propose a novel pupil detection pipeline (known as Edge Points Selector “EPS”) which is suitable even for partial occlusion, lighting, and specular reflection. EPS consists of pupil area detection, edge selection, and ellipse fitting. For the first time, we find the suitable Haar-like feature of 2D-pupil and a new pupil edge feature in the local pupil area, and integrate them into the proposed pipeline. EPS was compared with two state-of-art methods on 130856 images in this work. Within an error threshold of 5 pixels, our method outperforms the comparison algorithms by 33.8% and 19.4%, respectively, on overage.

中文翻译：

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EPS：具有 Haar 特征和形态像素模式的鲁棒瞳孔边缘点选择

瞳孔参数是认知科学和人机交互等许多应用的重要基础。现有方法仍然受到各种挑战的影响。我们提出了一种新颖的瞳孔检测管道（称为边缘点选择器“EPS”），它甚至适用于部分遮挡、照明和镜面反射。EPS由瞳孔区域检测、边缘选择和椭圆拟合组成。我们第一次在局部瞳孔区域中找到了合适的 2D 瞳孔类 Haar 特征和新的瞳孔边缘特征，并将它们整合到所提出的管道中。EPS 在 130 上与两种最先进的方法进行了比较这项工作中有 856 幅图像。在 5 个像素的误差阈值内，我们的方法在超龄方面分别优于比较算法 33.8% 和 19.4%。