Porta Cuéllar, SoniaBossavit, BenoîtCabeza Laguna, RafaelLarumbe Bergera, AndoniGarde Lecumberri, GonzaloVillanueva Larre, Arantxa2020-07-022021-03-052019S. Porta, B. Bossavit, R. Cabeza, A. Larumbe-Bergera, G. Garde and A. Villanueva, 'U2Eyes: A Binocular Dataset for Eye Tracking and Gaze Estimation,' 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2019, pp. 3660-3664, doi: 10.1109/ICCVW.2019.00451.978-1-7281-5023-92473-994410.1109/ICCVW.2019.00451https://academica-e.unavarra.es/handle/2454/37285Theory shows that huge amount of labelled data are needed in order to achieve reliable classification/regression methods when using deep/machine learning techniques. However, in the eye tracking field, manual annotation is not a feasible option due to the wide variability to be covered. Hence, techniques devoted to synthesizing images show up as an opportunity to provide vast amounts of annotated data. Considering that the well-known UnityEyes tool provides a framework to generate single eye images and taking into account that both eyes information can contribute to improve gaze estimation accuracy we present U2Eyes dataset, that is publicly available. It comprehends about 6 million of synthetic images containing binocular data. Furthermore, the physiology of the eye model employed is improved, simplified dynamics of binocular vision are incorporated and more detailed 2D and 3D labelled data are provided. Additionally, an example of application of the dataset is shown as work in progress. Employing U2Eyes as training framework Supervised Descent Method (SDM) is used for eyelids segmentation. The model obtained as result of the training process is then applied on real images from GI4E dataset showing promising results.5 p.application/pdfeng© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.Eye trackingLow resolutionBinocular datasetUnityAnnotationinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess