An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices
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.
Citation
Lei , Y , He , S , Khamis , M & Ye , J 2024 , ' An end-to-end review of gaze estimation and its interactive applications on handheld mobile devices ' , ACM Computing Surveys , vol. 56 , no. 2 , 34 . https://doi.org/10.1145/3606947
Publication
ACM Computing Surveys
Status
Peer reviewed
ISSN
0360-0300Type
Journal article
Collections
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