We assess the feasibility of conducting web-based eye-tracking experiments with children using two methods of webcam-based eye-tracking: automatic gaze estimation with the WebGazer.js algorithm and hand annotation of gaze direction from recorded webcam videos. Experiment 1 directly compares the two methods in a visual-world language task with five to six year-old children. Experiment 2 more precisely investigates WebGazer.js’ spatiotemporal resolution with four to twelve year-old children in a visual-fixation task. We find that it is possible to conduct web-based eye-tracking experiments with children in both supervised (Experiment 1) and unsupervised (Experiment 2) settings – however, the webcam eye-tracking methods differ in their sensitivity and accuracy. Webcam video annotation is well-suited to detecting fine-grained looking effects relevant to child language researchers. In contrast, WebGazer.js gaze estimates appear noisier and less temporally precise. We discuss the advantages and disadvantages of each method and provide recommendations for researchers conducting child eye-tracking studies online.

中文翻译：

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评估用于儿童语言研究的两种基于网络摄像头的眼球追踪方法


我们评估了使用两种基于网络摄像头的眼动追踪方法对儿童进行基于网络的眼动追踪实验的可行性：使用 WebGazer.js 算法进行自动注视估计，以及从录制的网络摄像头视频中手动注释注视方向。实验 1 直接比较了五到六岁儿童视觉世界语言任务中的两种方法。实验 2 更精确地研究了 WebGazer.js 在视觉固定任务中对 4 到 12 岁儿童的时空分辨率。我们发现可以在有监督（实验 1）和无监督（实验 2）的环境中对儿童进行基于网络的眼动追踪实验 - 然而，网络摄像头眼动追踪方法的灵敏度和准确性有所不同。网络摄像头视频注释非常适合检测与儿童语言研究人员相关的细粒度外观效果。相比之下，WebGazer.js 的注视估计显得噪声较大且时间精度较低。我们讨论每种方法的优点和缺点，并为研究人员在线进行儿童眼球追踪研究提供建议。