Department of Electrical and Computer Engineering, University of Toronto, Toronto, ON M5S 1A4, Canada.
Ophthalmology and Vision Sciences, University of Toronto, Toronto, ON M5T 3A9, Canada.
Sensors (Basel). 2020 Jan 19;20(2):543. doi: 10.3390/s20020543.
This paper describes a low-cost, robust, and accurate remote eye-tracking system that uses an industrial prototype smartphone with integrated infrared illumination and camera. Numerous studies have demonstrated the beneficial use of eye-tracking in domains such as neurological and neuropsychiatric testing, advertising evaluation, pilot training, and automotive safety. Remote eye-tracking on a smartphone could enable the significant growth in the deployment of applications in these domains. Our system uses a 3D gaze-estimation model that enables accurate point-of-gaze (PoG) estimation with free head and device motion. To accurately determine the input eye features (pupil center and corneal reflections), the system uses Convolutional Neural Networks (CNNs) together with a novel center-of-mass output layer. The use of CNNs improves the system's robustness to the significant variability in the appearance of eye-images found in handheld eye trackers. The system was tested with 8 subjects with the device free to move in their hands and produced a gaze bias of 0.72°. Our hybrid approach that uses artificial illumination, a 3D gaze-estimation model, and a CNN feature extractor achieved an accuracy that is significantly (400%) better than current eye-tracking systems on smartphones that use natural illumination and machine-learning techniques to estimate the PoG.
本文描述了一种低成本、鲁棒且精确的远程眼动追踪系统，该系统使用带有集成红外照明和摄像头的工业原型智能手机。许多研究已经证明了眼动追踪在神经学和神经精神病学测试、广告评估、飞行员培训和汽车安全等领域的有益用途。智能手机上的远程眼动追踪可以极大地促进这些领域应用的部署。我们的系统使用了 3D 注视估计模型，该模型支持在自由头部和设备运动的情况下进行精确的注视点（PoG）估计。为了准确确定输入的眼睛特征（瞳孔中心和角膜反射），系统使用卷积神经网络（CNN）和一个新颖的质心输出层。CNN 的使用提高了系统对手持眼动追踪器中发现的眼睛图像外观显著变化的鲁棒性。该系统在 8 名受试者中进行了测试，他们可以自由地在手中移动设备，产生的注视偏差为 0.72°。我们的混合方法使用人工照明、3D 注视估计模型和 CNN 特征提取器，其精度比目前使用自然照明和机器学习技术来估计 PoG 的智能手机上的眼动追踪系统显著提高了 400%。
