Cabeza Laguna, Rafael
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Cabeza Laguna
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Rafael
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Ingeniería Eléctrica, Electrónica y de Comunicación
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ISC. Institute of Smart Cities
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Publication Open Access Beyond basic tuning: exploring discrepancies in user and setup calibration for gaze estimation(ACM, 2024-06-04) Garde Lecumberri, Gonzalo; Armendáriz Armenteros, José María; Beruete Cerezo, Rubén; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio IngeniaritzaCalibrating 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.Publication Open Access Hybrid method based on topography for robust detection of iris center and eye corners(ACM (Association for Computing Machinery), 2013) Villanueva Larre, Arantxa; Ponz Sarvisé, Victoria; Sesma Sánchez, Laura; Ariz Galilea, Mikel; Porta Cuéllar, Sonia; Cabeza Laguna, Rafael; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio IngeniaritzarenA multi-stage procedure to detect eye features is presented. Multiresolution and topographic classification are used to detect the iris center. The eye corner is calculated combining valley detection and eyelid curve extraction. The algorithm is tested in the BioID database and in a proprietary database containing more than 1200 images. The results show that the suggested algorithm is robust and accurate. Regarding the iris center our method obtains the best average behavior for the BioID database compared to other available algorithms. Additional contributions are that our algorithm functions in real time and does not require complex post processing stages.