InvisibleEye
InvisibleEye
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DOI：
10.1145/3130971
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发表时间：
2017-09
影响因子：
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通讯作者：
Marc Tonsen;Julian Steil;Yusuke Sugano;A. Bulling
中科院分区：
文献类型：
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作者：
Marc Tonsen;Julian Steil;Yusuke Sugano;A. Bulling
对日常人类注视行为的分析在普适计算方面具有重大潜力，基于注视的人机交互、专注用户界面以及基于眼睛的用户建模方面的大量研究工作都证明了这一点。然而，当前的移动眼动仪仍然具有侵入性，这不仅使它们佩戴起来不舒适，在日常生活中不被社会接受，而且还阻碍了它们在社会科学和行为科学中的广泛应用。为了应对这些挑战，我们提出了InvisibleEye，这是一种用于移动眼动追踪的新方法，它使用可以完全嵌入普通眼镜框的毫米级RGB摄像头。为了弥补摄像头仅几个像素的低图像分辨率，我们的方法使用多个摄像头来捕捉眼睛的不同视角，并使用基于学习的注视估计方法直接从眼睛图像回归到注视方向。我们对我们的系统进行了原型实现，并在三个大规模、越来越贴近现实且具有挑战性的数据集上对其性能进行了表征：1）使用最近的计算机图形学眼部区域模型合成的眼睛图像；2）在受控照明条件下对17名参与者记录的真实眼睛图像；3）在移动环境下对四名参与者在四次记录过程中记录的眼睛图像。我们表明，InvisibleEye使用四个分辨率仅为5×5像素的摄像头，实现了1.79°的最高个体特定注视估计精度。我们的评估不仅证明了这种新方法的可行性，更重要的是，强调了它在最终实现隐形移动眼动追踪和普适专注用户界面愿景方面的巨大潜力。
Analysis of everyday human gaze behaviour has significant potential for ubiquitous computing, as evidenced by a large body of work in gaze-based human-computer interaction, attentive user interfaces, and eye-based user modelling. However, current mobile eye trackers are still obtrusive, which not only makes them uncomfortable to wear and socially unacceptable in daily life, but also prevents them from being widely adopted in the social and behavioural sciences. To address these challenges we present InvisibleEye, a novel approach for mobile eye tracking that uses millimetre-size RGB cameras that can be fully embedded into normal glasses frames. To compensate for the cameras’ low image resolution of only a few pixels, our approach uses multiple cameras to capture different views of the eye, as well as learning-based gaze estimation to directly regress from eye images to gaze directions. We prototypically implement our system and characterise its performance on three large-scale, increasingly realistic, and thus challenging datasets: 1) eye images synthesised using a recent computer graphics eye region model, 2) real eye images recorded of 17 participants under controlled lighting, and 3) eye images recorded of four participants over the course of four recording sessions in a mobile setting. We show that InvisibleEye achieves a top person-specific gaze estimation accuracy of 1.79° using four cameras with a resolution of only 5 × 5 pixels. Our evaluations not only demonstrate the feasibility of this novel approach but, more importantly, underline its significant potential for finally realising the vision of invisible mobile eye tracking and pervasive attentive user interfaces.