research-article

Statistical models of appearance for eye tracking and eye-blink detection and measurement

Authors:

I. Bacivarov,

M. Ionita, and

P. CorcoranAuthors Info & Claims

IEEE Transactions on Consumer Electronics, Volume 54, Issue 3

Pages 1312 - 1320

https://doi.org/10.1109/TCE.2008.4637622

Published: 01 August 2008 Publication History

Abstract

A statistical active appearance model (AAM) is developed to track and detect eye blinking. The model has been designed to be robust to variations of head pose or gaze. In particular we analyze and determine the model parameters which encode the variations caused by blinking. This global model is further extended using a series of sub-models to enable independent modeling and tracking of the two eye regions. Several methods to enable measurement and detection of eye-blink are proposed and evaluated. The results of various tests on different image databases are presented to validate each model.

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Hutt SD'Mello S(2022)Evaluating Calibration-free Webcam-based Eye Tracking for Gaze-based User ModelingProceedings of the 2022 International Conference on Multimodal Interaction10.1145/3536221.3556580(224-235)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3536221.3556580
Hutt SStewart AGregg JMattingly SD'Mello S(2022)Feasibility of Longitudinal Eye-Gaze Tracking in the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/35308896:ETRA(1-21)Online publication date: 13-May-2022
https://dl.acm.org/doi/10.1145/3530889
Dutta AMondal ADey NSen SMoraru LHassanien A(2020)Vision Tracking: A Survey of the State-of-the-ArtSN Computer Science10.1007/s42979-019-0059-z1:1Online publication date: 11-Jan-2020
https://dl.acm.org/doi/10.1007/s42979-019-0059-z
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Index Terms

Statistical models of appearance for eye tracking and eye-blink detection and measurement
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Computer vision tasks
        Scene understanding

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Consumer Electronics

IEEE Transactions on Consumer Electronics Volume 54, Issue 3

August 2008

533 pages

ISSN:0098-3063

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Publisher

IEEE Press

Publication History

Published: 01 August 2008

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Hutt SD'Mello S(2022)Evaluating Calibration-free Webcam-based Eye Tracking for Gaze-based User ModelingProceedings of the 2022 International Conference on Multimodal Interaction10.1145/3536221.3556580(224-235)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1145/3536221.3556580
Hutt SStewart AGregg JMattingly SD'Mello S(2022)Feasibility of Longitudinal Eye-Gaze Tracking in the WorkplaceProceedings of the ACM on Human-Computer Interaction10.1145/35308896:ETRA(1-21)Online publication date: 13-May-2022
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Dutta AMondal ADey NSen SMoraru LHassanien A(2020)Vision Tracking: A Survey of the State-of-the-ArtSN Computer Science10.1007/s42979-019-0059-z1:1Online publication date: 11-Jan-2020
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Kowalczyk PSawicki D(2019)Blink and wink detection as a control tool in multimodal interactionMultimedia Tools and Applications10.1007/s11042-018-6554-878:10(13749-13765)Online publication date: 1-May-2019
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Yamamoto KToyoda KOhtsuki T(2017)Driver's blink detection using Doppler sensor2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)10.1109/PIMRC.2017.8292496(1-6)Online publication date: 8-Oct-2017
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Tamba CHayashi HOhtsuki T(2016)Improvement of Blink Detection Using a Doppler Sensor Based on CFAR Processing2016 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOCOM.2016.7841887(1-6)Online publication date: 4-Dec-2016
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Pupil vs. Eyelid: Evaluating the Accuracy of Blink Detection in Pupil-based Eye Tracking Devices

Eye detection and coarse localization of pupil for video-based eye tracking systems

Eye&Head: Synergetic Eye and Head Movement for Gaze Pointing and Selection

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