skip to main content
10.1145/2857491.2857509acmconferencesArticle/Chapter ViewAbstractPublication PagesetraConference Proceedingsconference-collections
short-paper

Gaussian processes as an alternative to polynomial gaze estimation functions

Published: 14 March 2016 Publication History

Abstract

Interpolation-based methods are widely used for gaze estimation due to their simplicity. In particular, feature-based methods that map the image eye features to gaze, are very popular. The most spread regression function used in this kind of method is the polynomial regression. In this paper, we present an alternative regression function to estimate gaze: the Gaussian regression. We show how the Gaussian processes can better adapt to the non-linear behavior of the eye movement, providing higher gaze estimation accuracies. The Gaussian regression is compared, in a simulated environment, to the polynomial regression, when using the same mapping features, the normalized pupil center-corneal reflection and pupil center-eye corners vectors. This comparison is done for three different screen sizes. The results show that for larger screens, where wider gaze angles are required, i.e., the non-linear behavior of the eye is more present, the outperformance of the Gaussian regression is more evident. Furthermore, we can conclude that, for both types of regressions, the gaze estimation accuracy increases for smaller screens, where the eye movements are more linear.

References

[1]
Blignaut, P. 2013. A new mapping function to improve the accuracy of a video-based eye tracker. In Proc. SAICSIT '13, ACM, NY, USA, 56--59.
[2]
Blignaut, P. 2014. Mapping the pupil-glint vector to gaze coordinates in a simple video-based eye tracker. Journal of Eye Mov. Research 7(1), 4, 1--11.
[3]
Böhme, M., Dorr, M., Graw, M., Martinetz, T., and Barth, E. 2008. A software framework for simulating eye trackers. In Proc. ETRA '08, ACM, 251--258.
[4]
Cerrolaza, J. J., Villanueva, A., and Cabeza, R. 2012. Study of polynomial mapping functions in video-oculography eye trackers. ACM Trans. Comput.-Hum. Interact. 19, 2 (July), 10:1--10:25.
[5]
Hansen, D., and Ji, Q. 2010. In the eye of the beholder: A survey of models for eyes and gaze. IEEE Trans. Pattern Anal. Mach. Intell. 32, 3, 478--500.
[6]
Morimoto, C., and Mimica, M. 2005. Eye gaze tracking techniques for interactive applications. Computer Vision and Image Understanding 98, 1, 4--24.
[7]
Rasmussen, C. E., and Nickisch, H. 2010. Gaussian processes for machine learning (gpml) toolbox. The Journal of Machine Learning Research 11, 3011--3015.
[8]
Rasmussen, C. E., and Williams, C. K. I. 2006. Gaussian Processes for Machine Learning. the MIT Press.
[9]
Sesma, L., Villanueva, A., and Cabeza, R. 2012. Evaluation of pupil center-eye corner vector for gaze estimation using a web cam. In Proc. ETRA '12, ACM, New York, NY, USA, 217--220.
[10]
Zhang, Y., Bulling, A., and Gellersen, H. 2014. Pupil-canthi-ratio: A calibration-free method for tracking horizontal gaze direction. In Proc. AVI '14, ACM, New York, NY, USA, 129--132.
[11]
Zhu, Z., and Ji, Q. 2004. Eye and gaze tracking for interactive graphic display. Mach. Vision Appl. 15, 3 (July), 139--148.
[12]
Zhu, Z., Ji, Q., and Bennett, K. 2006. Nonlinear eye gaze mapping function estimation via support vector regression. In Proc. ICPR 2006, vol. 1, 1132--1135.

Cited By

View all
  • (2023)An Eye Tracking and Brain–Computer Interface-Based Human–Environment Interactive System for Amyotrophic Lateral Sclerosis PatientsIEEE Sensors Journal10.1109/JSEN.2022.322387823:20(24095-24106)Online publication date: 15-Oct-2023
  • (2022)High-Accuracy 3D Gaze Estimation with Efficient Recalibration for Head-Mounted Gaze Tracking SystemsSensors10.3390/s2212435722:12(4357)Online publication date: 8-Jun-2022
  • (2022)A New Eye-tracking Method with Image Feature Based Model for Mobile Devices2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta)10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00275(1902-1909)Online publication date: Dec-2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
ETRA '16: Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications
March 2016
378 pages
ISBN:9781450341257
DOI:10.1145/2857491
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 March 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. gaussian process
  2. gaze estimation
  3. polynomials

Qualifiers

  • Short-paper

Conference

ETRA '16
ETRA '16: 2016 Symposium on Eye Tracking Research and Applications
March 14 - 17, 2016
South Carolina, Charleston

Acceptance Rates

Overall Acceptance Rate 69 of 137 submissions, 50%

Upcoming Conference

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)14
  • Downloads (Last 6 weeks)0
Reflects downloads up to 06 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2023)An Eye Tracking and Brain–Computer Interface-Based Human–Environment Interactive System for Amyotrophic Lateral Sclerosis PatientsIEEE Sensors Journal10.1109/JSEN.2022.322387823:20(24095-24106)Online publication date: 15-Oct-2023
  • (2022)High-Accuracy 3D Gaze Estimation with Efficient Recalibration for Head-Mounted Gaze Tracking SystemsSensors10.3390/s2212435722:12(4357)Online publication date: 8-Jun-2022
  • (2022)A New Eye-tracking Method with Image Feature Based Model for Mobile Devices2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta)10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00275(1902-1909)Online publication date: Dec-2022
  • (2020)Neural networks for optical vector and eye ball parameter estimationACM Symposium on Eye Tracking Research and Applications10.1145/3379156.3391346(1-5)Online publication date: 2-Jun-2020
  • (2019)Toward Precise Gaze Estimation for Mobile Head-Mounted Gaze Tracking SystemsIEEE Transactions on Industrial Informatics10.1109/TII.2018.286795215:5(2660-2672)Online publication date: May-2019
  • (2018)3D gaze estimation in the scene volume with a head-mounted eye trackerProceedings of the Workshop on Communication by Gaze Interaction10.1145/3206343.3206351(1-9)Online publication date: 15-Jun-2018
  • (2017)ScreenGlintProceedings of the 2017 CHI Conference on Human Factors in Computing Systems10.1145/3025453.3025794(2546-2557)Online publication date: 2-May-2017
  • (2016)Low-cost eye-tracking glasses with real-time head rotation compensation2016 10th International Conference on Sensing Technology (ICST)10.1109/ICSensT.2016.7796336(1-5)Online publication date: Nov-2016

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media