An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile Devices

TL;DR

This paper provides a comprehensive review of gaze estimation techniques and their applications on mobile devices, highlighting recent advances, challenges, and future research directions in the field.

Contribution

It offers an end-to-end overview of gaze estimation methods, from sensors to applications, integrating recent deep learning advancements and identifying key research challenges.

Findings

Deep learning has significantly improved gaze estimation accuracy.

Mobile devices now support real-time gaze-based interactions.

The review highlights open challenges and future opportunities in the field.

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 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.