research-article

EllSeg-Gen, towards Domain Generalization for Head-Mounted Eyetracking

Authors:

Rakshit S. Kothari,

Reynold J. Bailey,

Christopher Kanan,

Gabriel J. DiazAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 6, Issue ETRA

Article No.: 139, Pages 1 - 17

https://doi.org/10.1145/3530880

Published: 13 May 2022 Publication History

Abstract

The study of human gaze behavior in natural contexts requires algorithms for gaze estimation that are robust to a wide range of imaging conditions. However, algorithms often fail to identify features such as the iris and pupil centroid in the presence of reflective artifacts and occlusions. Previous work has shown that convolutional networks excel at extracting gaze features despite the presence of such artifacts. However, these networks often perform poorly on data unseen during training. This work follows the intuition that jointly training a convolutional network with multiple datasets learns a generalized representation of eye parts. We compare the performance of a single model trained with multiple datasets against a pool of models trained on individual datasets. Results indicate that models tested on datasets in which eye images exhibit higher appearance variability benefit from multiset training. In contrast, dataset-specific models generalize better onto eye images with lower appearance variability.

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Cited By

Gundler CTemmen MGulberti APötter-Nerger MÜckert F(2024)Improving Eye-Tracking Data Quality: A Framework for Reproducible Evaluation of Detection AlgorithmsSensors10.3390/s2409268824:9(2688)Online publication date: 24-Apr-2024
https://doi.org/10.3390/s24092688
Barkevich KBailey RDiaz G(2024)Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual RealityProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36547057:2(1-16)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3654705
Maquiling VByrne SNiehorster DNyström MKasneci E(2024)Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)Proceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36547047:2(1-16)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3654704
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Index Terms

EllSeg-Gen, towards Domain Generalization for Head-Mounted Eyetracking
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation
  2. Machine learning

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 6, Issue ETRA

ETRA

May 2022

198 pages

EISSN:2573-0142

DOI:10.1145/3537904

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Apple Inc., United States

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Published: 13 May 2022

Published in PACMHCI Volume 6, Issue ETRA

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Cited By

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https://doi.org/10.3390/s24092688
Barkevich KBailey RDiaz G(2024)Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual RealityProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36547057:2(1-16)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3654705
Maquiling VByrne SNiehorster DNyström MKasneci E(2024)Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)Proceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36547047:2(1-16)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3654704
Nguyen VBailey RDiaz GMa CFix AOrorbia A(2024)Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking SystemsProceedings of the ACM on Computer Graphics and Interactive Techniques10.1145/36547037:2(1-17)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3654703
Hallgarten PSendhilnathan NZhang TSood EJonker T(2024)GEARS: Generalizable Multi-Purpose Embeddings for Gaze and Hand Data in VR InteractionsProceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3627043.3659551(279-289)Online publication date: 22-Jun-2024
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Biswas ALescroart M(2023)A framework for generalizable neural networks for robust estimation of eyelids and pupilsBehavior Research Methods10.3758/s13428-023-02266-356:4(3959-3981)Online publication date: 28-Nov-2023
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