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.

翻译：近年来，我们观察到在手持移动设备上，利用视线作为单一或辅助交互模式的交互系统日益增多。这一趋势得益于设备计算能力的增强、摄像头分辨率与容量的提升，以及先进机器学习技术（尤其是深度学习）所实现的更高视线估计精度。随着相关文献的快速发展，亟需对当前技术现状进行综述，界定研究边界，并识别视线估计与交互领域的关键挑战与机遇。本文旨在通过呈现该领域的端到端全景视角——涵盖视线捕捉传感器、视线估计流程、深度学习技术及视线交互应用——来满足这一需求。