Garde Lecumberri, GonzaloLarumbe Bergera, AndoniBossavit, BenoîtPorta Cuéllar, SoniaCabeza Laguna, RafaelVillanueva Larre, Arantxa2021-12-092021-12-0920211424-8220 (Electronic)10.3390/s21155109https://academica-e.unavarra.es/handle/2454/41210Subject 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 systems21 p.application/pdfeng© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.Gaze-estimationCalibrationLow-resolutionTheoretical analysisinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/openAccess