An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices
Yaxiong Lei, Shijing He, Mohamed Khamis, Juan Ye
Abstract
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 article 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.