Publication:
Beyond basic tuning: exploring discrepancies in user and setup calibration for gaze estimation

Date

2024-06-04

Authors

Armendáriz Armenteros, José María
Beruete Cerezo, Rubén

Director

Publisher

ACM
Acceso abierto / Sarbide irekia
Contribución a congreso / Biltzarrerako ekarpena
Versión publicada / Argitaratu den bertsioa

Project identifier

Impacto
OpenAlexGoogle Scholar
No disponible en Scopus

Abstract

Calibrating gaze estimation models is crucial to maximize the effectiveness of these systems, although its implementation also poses challenges related to usability. Therefore, the simplification of this process is key. In this work, we dissect the impact of calibration due to both the environment and the user in gaze estimation models that employ general-purpose devices. We aim to replicate a workflow close to the final application by starting with pre-trained models and subsequently calibrating them using different strategies, testing under various camera arrangements and user-specific variability. The results indicate differentiation between the impact due to the user and the setup, being the components due to the users a slightly more pronounced impact than those related to the setup, opening the door to understanding calibration as a composite process. In any case, the development of calibration-free remote gaze estimation solutions remains a great challenge, given the crucial role of calibration.

Description

Keywords

Calibration algorithms, Computer vision, Gaze estimation, Low-cost and smartphone systems

Department

Ingeniería Eléctrica, Electrónica y de Comunicación / Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza

Faculty/School

Degree

Doctorate program

item.page.cita

Garde, G., Armendariz, J. M., Cerezo, R. B., Cabeza, R., Villanueva, A. (2024) Beyond basic tuning: exploring discrepancies in user and setup calibration for gaze estimation. In Mohamed Khamis and Yusuke Sugano (Eds.), ETRA '24: Proceedings of the 2024 Symposium on Eye Tracking Research and Applications (pp. 1-8). Association for Computing Machinery. https://doi.org/10.1145/3649902.3653346

item.page.rights

© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.

Licencia

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