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Ultra-Low-Power Gaze Tracking for Virtual Reality

Authors:

Tianxing Li,

Qiang Liu,

Xia ZhouAuthors Info & Claims

GetMobile: Mobile Computing and Communications, Volume 22, Issue 3

Pages 27 - 31

https://doi.org/10.1145/3308755.3308765

Published: 17 January 2019 Publication History

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Abstract

LiGaze is a low-cost, low-power approach to gaze tracking tailored to virtual reality (VR). It relies on a few low-cost photodiodes, eliminating the need for cameras and active infrared emitters. Reusing light from the VR screen, LiGaze leverages photodiodes around a VR lens to measure reflected screen light in different directions. It then infers gaze direction by exploiting the pupil's light absorption property. The core of LiGaze is to deal with screen light dynamics and extract changes in reflected light related to pupil movement. LiGaze infers a 3D gaze vector on the fly using a lightweight regression algorithm. Compared to the eye tracker of an existing VR headset (FOVE), LiGaze achieves 6.3° and 10.1° mean within-user and cross-user accuracy. Its sensing and computation consume 791 W in total and thus can be completely powered by a credit card-size solar cell harvesting energy from indoor lighting. LiGaze's simplicity and ultra-low power make it applicable in a wide range of VR headsets to better unleash VR's potential.

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Cited By

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Ghosh SDhall AHayat MKnibbe JJi Q(2024)Automatic Gaze Analysis: A Survey of Deep Learning Based ApproachesIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.332133746:1(61-84)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TPAMI.2023.3321337
Satriawan AHermawan ALuckyarno YYun J(2023)Predicting Future Eye Gaze Using Inertial SensorsIEEE Access10.1109/ACCESS.2023.329241111(67482-67497)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3292411
Sun SWang JZhang MYuan YNing YMa DNiu PGong YYang XPang W(2022)Eye-Tracking Monitoring Based on PMUT ArraysJournal of Microelectromechanical Systems10.1109/JMEMS.2021.312537731:1(45-53)Online publication date: Feb-2022
https://doi.org/10.1109/JMEMS.2021.3125377

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Published In

cover image GetMobile: Mobile Computing and Communications

GetMobile: Mobile Computing and Communications Volume 22, Issue 3

September 2018

34 pages

ISSN:2375-0529

EISSN:2375-0537

DOI:10.1145/3308755

Editor:
Eyal de Lara
University of Toronto

Issue’s Table of Contents

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 January 2019

Published in SIGMOBILE-GETMOBILE Volume 22, Issue 3

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Ghosh SDhall AHayat MKnibbe JJi Q(2024)Automatic Gaze Analysis: A Survey of Deep Learning Based ApproachesIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2023.332133746:1(61-84)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TPAMI.2023.3321337
Satriawan AHermawan ALuckyarno YYun J(2023)Predicting Future Eye Gaze Using Inertial SensorsIEEE Access10.1109/ACCESS.2023.329241111(67482-67497)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3292411
Sun SWang JZhang MYuan YNing YMa DNiu PGong YYang XPang W(2022)Eye-Tracking Monitoring Based on PMUT ArraysJournal of Microelectromechanical Systems10.1109/JMEMS.2021.312537731:1(45-53)Online publication date: Feb-2022
https://doi.org/10.1109/JMEMS.2021.3125377

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