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Supervised descent method (SDM) applied to accurate pupil detection in off-the-shelf eye tracking systems

Authors:

Andoni Larumbe,

Arantxa VillanuevaAuthors Info & Claims

ETRA '18: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications

Article No.: 7, Pages 1 - 8

https://doi.org/10.1145/3204493.3204551

Published: 14 June 2018 Publication History

Abstract

The precise detection of pupil/iris center is key to estimate gaze accurately. This fact becomes specially challenging in low cost frameworks in which the algorithms employed for high performance systems fail. In the last years an outstanding effort has been made in order to apply training-based methods to low resolution images. In this paper, Supervised Descent Method (SDM) is applied to GI4E database. The 2D landmarks employed for training are the corners of the eyes and the pupil centers. In order to validate the algorithm proposed, a cross validation procedure is performed. The strategy employed for the training allows us to affirm that our method can potentially outperform the state of the art algorithms applied to the same dataset in terms of 2D accuracy. The promising results encourage to carry on in the study of training-based methods for eye tracking.

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Cited By

Al-Ikhsan HBachtiar F(2024)PGV: An Edge Operator Based Random Point Localization Algorithm for Eye Tracking System2024 11th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE)10.1109/ICITACEE62763.2024.10761950(59-64)Online publication date: 29-Aug-2024
https://doi.org/10.1109/ICITACEE62763.2024.10761950
Narcizo Fdos Santos FHansen D(2021)High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking MethodsVision10.3390/vision50300415:3(41)Online publication date: 15-Sep-2021
https://doi.org/10.3390/vision5030041
Larumbe-Bergera AGarde GPorta SCabeza RVillanueva A(2021)Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural NetworksSensors10.3390/s2120684721:20(6847)Online publication date: 15-Oct-2021
https://doi.org/10.3390/s21206847
Show More Cited By

Index Terms

Supervised descent method (SDM) applied to accurate pupil detection in off-the-shelf eye tracking systems
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression

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Information & Contributors

Information

Published In

cover image ACM Conferences

ETRA '18: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications

June 2018

595 pages

ISBN:9781450357067

DOI:10.1145/3204493

Conference Chairs:
Bonita Sharif
Youngstown State University
,
Krzysztof Krejtz
University of Social Sciences and Humanities, Warsaw, Poland

Copyright © 2018 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2018

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Spanish Ministry of Economy, Industry and Competitiveness
Spain Ministry of Economy, Industry and Competitiveness

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ETRA '18

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ETRA '18: 2018 Symposium on Eye Tracking Research and Applications

June 14 - 17, 2018

Warsaw, Poland

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Cited By

Al-Ikhsan HBachtiar F(2024)PGV: An Edge Operator Based Random Point Localization Algorithm for Eye Tracking System2024 11th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE)10.1109/ICITACEE62763.2024.10761950(59-64)Online publication date: 29-Aug-2024
https://doi.org/10.1109/ICITACEE62763.2024.10761950
Narcizo Fdos Santos FHansen D(2021)High-Accuracy Gaze Estimation for Interpolation-Based Eye-Tracking MethodsVision10.3390/vision50300415:3(41)Online publication date: 15-Sep-2021
https://doi.org/10.3390/vision5030041
Larumbe-Bergera AGarde GPorta SCabeza RVillanueva A(2021)Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural NetworksSensors10.3390/s2120684721:20(6847)Online publication date: 15-Oct-2021
https://doi.org/10.3390/s21206847
Fuhl WSchneider JKasneci E(2021)1000 Pupil Segmentations in a Second using Haar Like Features and Statistical Learning2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW54120.2021.00386(3459-3469)Online publication date: Oct-2021
https://doi.org/10.1109/ICCVW54120.2021.00386
Manuri FSanna APetrucci C(2020)PDIF: Pupil Detection After Isolation and FittingIEEE Access10.1109/ACCESS.2020.29730058(30826-30837)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2973005
Larumbe-Bergera APorta SCabeza RVillanueva AKrejtz KSharif B(2019)SeTAProceedings of the 11th ACM Symposium on Eye Tracking Research & Applications10.1145/3314111.3319830(1-5)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3314111.3319830

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