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Article

Low-Cost Eye Tracking Calibration: A Knowledge-Based Study †

by
Gonzalo Garde
1,*,
Andoni Larumbe-Bergera
1,
Benoît Bossavit
2,
Sonia Porta
1,
Rafael Cabeza
1 and
Arantxa Villanueva
1
1
Department of Electrical, Electronic and Communications Engineering, Arrosadia Campus, Public University of Navarre, 31006 Pamplona, Spain
2
School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin, College Green, D02 PN40 Dublin 2, Ireland
*
Author to whom correspondence should be addressed.
†
This paper is an extended version of our paper published in Garde, G.; Larumbe-Bergera, A.; Porta, S.; Cabeza, R.; Villanueva, A. Synthetic Gaze Data Augmentation for Improved User Calibration. In Proceedings of the International Conference on Pattern Recognition (ICPR), Virtual Event, 10–15 January 2021.
Sensors 2021, 21(15), 5109; https://doi.org/10.3390/s21155109
Submission received: 8 June 2021 / Revised: 20 July 2021 / Accepted: 22 July 2021 / Published: 28 July 2021
(This article belongs to the Special Issue Eye Tracking Techniques, Applications, and Challenges)
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Abstract

Subject calibration has been demonstrated to improve the accuracy in high-performance eye trackers. However, the true weight of calibration in off-the-shelf eye tracking solutions is still not addressed. In this work, a theoretical framework to measure the effects of calibration in deep learning-based gaze estimation is proposed for low-resolution systems. To this end, features extracted from the synthetic U2Eyes dataset are used in a fully connected network in order to isolate the effect of specific user’s features, such as kappa angles. Then, the impact of system calibration in a real setup employing I2Head dataset images is studied. The obtained results show accuracy improvements over 50%, probing that calibration is a key process also in low-resolution gaze estimation scenarios. Furthermore, we show that after calibration accuracy values close to those obtained by high-resolution systems, in the range of 0.7°, could be theoretically obtained if a careful selection of image features was performed, demonstrating significant room for improvement for off-the-shelf eye tracking systems.
Keywords: gaze-estimation; calibration; low-resolution; theoretical analysis gaze-estimation; calibration; low-resolution; theoretical analysis

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MDPI and ACS Style

Garde, G.; Larumbe-Bergera, A.; Bossavit, B.; Porta, S.; Cabeza, R.; Villanueva, A. Low-Cost Eye Tracking Calibration: A Knowledge-Based Study. Sensors 2021, 21, 5109. https://doi.org/10.3390/s21155109

AMA Style

Garde G, Larumbe-Bergera A, Bossavit B, Porta S, Cabeza R, Villanueva A. Low-Cost Eye Tracking Calibration: A Knowledge-Based Study. Sensors. 2021; 21(15):5109. https://doi.org/10.3390/s21155109

Chicago/Turabian Style

Garde, Gonzalo, Andoni Larumbe-Bergera, Benoît Bossavit, Sonia Porta, Rafael Cabeza, and Arantxa Villanueva. 2021. "Low-Cost Eye Tracking Calibration: A Knowledge-Based Study" Sensors 21, no. 15: 5109. https://doi.org/10.3390/s21155109

APA Style

Garde, G., Larumbe-Bergera, A., Bossavit, B., Porta, S., Cabeza, R., & Villanueva, A. (2021). Low-Cost Eye Tracking Calibration: A Knowledge-Based Study. Sensors, 21(15), 5109. https://doi.org/10.3390/s21155109

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