Garde Lecumberri, GonzaloArmendáriz Armenteros, José MaríaBeruete Cerezo, RubénCabeza Laguna, RafaelVillanueva Larre, Arantxa2024-12-122024-12-122024-06-04Garde, 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/10.1145/3649902.3653346979-8-4007-0607-310.1145/3649902.3653346https://academica-e.unavarra.es/handle/2454/52705Calibrating 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.application/pdfeng© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution International 4.0 License.Calibration algorithmsComputer visionGaze estimationLow-cost and smartphone systemsinfo:eu-repo/semantics/conferenceObject2024-12-12info:eu-repo/semantics/openAccess