Eye Tracking in Augmented Spaces: a Deep Learning Approach

Peter Corcoran

Eye Tracking in Augmented Spaces: a Deep Learning Approach

Electrical & Electronic Engineering

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

The use of deep learning for estimating eye gaze in augmented spaces is investigated in this work. There are two primary ways of interacting with augmented spaces. The first involves the use of AR VR systems; the second involves devices that respond to the users gaze directly. This domain can overlap with AR VR environments but is not exclusive to them and contains its own unique set of issues. Deep learning methods for eye tracking that are capable of performing with minimal power consumption are investigated for both problems.

Original language	English (Ireland)
Title of host publication	2018 IEEE GAMES, ENTERTAINMENT, MEDIA CONFERENCE (GEM)
Publisher	IEEE
Number of pages	5
Publication status	Published - 1 Jan 2018

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Lemley, J;Kar, A;Corcoran, P

Cite this

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title = "Eye Tracking in Augmented Spaces: a Deep Learning Approach",

abstract = "The use of deep learning for estimating eye gaze in augmented spaces is investigated in this work. There are two primary ways of interacting with augmented spaces. The first involves the use of AR VR systems; the second involves devices that respond to the users gaze directly. This domain can overlap with AR VR environments but is not exclusive to them and contains its own unique set of issues. Deep learning methods for eye tracking that are capable of performing with minimal power consumption are investigated for both problems.",

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AB - The use of deep learning for estimating eye gaze in augmented spaces is investigated in this work. There are two primary ways of interacting with augmented spaces. The first involves the use of AR VR systems; the second involves devices that respond to the users gaze directly. This domain can overlap with AR VR environments but is not exclusive to them and contains its own unique set of issues. Deep learning methods for eye tracking that are capable of performing with minimal power consumption are investigated for both problems.

M3 - Conference Publication

BT - 2018 IEEE GAMES, ENTERTAINMENT, MEDIA CONFERENCE (GEM)

PB - IEEE

ER -

Eye Tracking in Augmented Spaces: a Deep Learning Approach

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this