We study gaze estimation on tablets; our key design goal is uncalibrated gaze estimation using the front-facing camera during natural use of tablets, where the posture and method of holding the tablet is not constrained. We collected the first large unconstrained gaze dataset of tablet users, labeled Rice TabletGaze dataset. The dataset consists of 51 subjects, each with 4 different postures and 35 gaze locations. Subjects vary in race, gender and in their need for prescription glasses, all of which might impact gaze estimation accuracy. Driven by our observations on the collected data, we present a baseline algorithm for automatic gaze estimation using multi-level HoG feature and Random Forests regressor. The TabletGaze algorithm achieves a mean error of 3.17 cm. We perform extensive evaluation on the impact of various factors such as dataset size, race, wearing glasses and user posture on the gaze estimation accuracy and make important observations about the impact of these factors.

本文研究了在平板电脑上的凝视估计。作者的主要设计目标是通过平板电脑的自然使用，利用前置摄像头进行非校准凝视估计，在此期间平板电脑的姿势和握持方式不受约束。作者收集了第一个大规模的无限制凝视数据集，并提出了一个名为TabletGaze的算法，它利用多级Hog特征和随机森林回归器进行凝视估计。与其他算法相比，这个算法的平均误差为3.17厘米。并在数据集大小、种族、佩戴眼镜和用户姿势等因素对凝视估计精度的影响方面进行了广泛的评估和重要观察。

TabletGaze: 移动平板上无限制外观为基础的凝视估计