An End-to-End Review of Gaze Estimation and its Interactive Applications
  on Handheld Mobile Devices
Yaxiong Lei Shijing He Mohamed Khamis Juan Ye
In recent years we have witnessed an increasing number of interactive systems
on handheld mobile devices which utilise gaze as a single or complementary
interaction modality. This trend is driven by the enhanced computational power
of these devices, higher resolution and capacity of their cameras, and improved
gaze estimation accuracy obtained from advanced machine learning techniques,
especially in deep learning. As the literature is fast progressing, there is a
pressing need to review the state of the art, delineate the boundary, and
identify the key research challenges and opportunities in gaze estimation and
interaction. This paper aims to serve this purpose by presenting an end-to-end
holistic view in this area, from gaze capturing sensors, to gaze estimation
workflows, to deep learning techniques, and to gaze interactive applications.