Ellipse fitting, an essential component in pupil or iris tracking based video oculography, is performed on previously segmented eye parts generated using various computer vision techniques. Several factors, such as occlusions due to eyelid shape, camera position or eyelashes, frequently break ellipse fitting algorithms that rely on well-defined pupil or iris edge segments. In this work, we propose training a convolutional neural network to directly segment entire elliptical structures and demonstrate that such a framework is robust to occlusions and offers superior pupil and iris tracking performance (at least 10% and 24% increase in pupil and iris center detection rate respectively within a two-pixel error margin) compared to using standard eye parts segmentation for multiple publicly available synthetic segmentation datasets.

中文翻译：

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EllSeg：用于稳健注视跟踪的椭圆分割框架

椭圆拟合是基于瞳孔或虹膜跟踪的视频眼部描记术的重要组成部分，它是在使用各种计算机视觉技术生成的先前分割的眼部部分上执行的。几个因素，例如由于眼睑形状、相机位置或睫毛引起的遮挡，经常会破坏依赖于明确定义的瞳孔或虹膜边缘段的椭圆拟合算法。在这项工作中，我们建议训练一个卷积神经网络来直接分割整个椭圆结构，并证明这样的框架对遮挡具有鲁棒性，并提供卓越的瞳孔和虹膜跟踪性能（瞳孔和虹膜中心检测至少增加 10% 和 24%）率分别在两个像素的误差范围内）与对多个公开可用的合成分割数据集使用标准眼部分割相比。