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Eye tracking technology in low resolution scenarios is not a completely solved issue to date. The possibility of using eye tracking in a mobile gadget is a challenging objective that would permit to spread this technology to non-explored fields. In this paper, a knowledge based approach is presented to solve gaze estimation in low resolution settings. The understanding of the high resolution paradigm permits to propose alternative models to solve gaze estimation. In this manner, three models are presented: a geometrical model, an interpolation model and a compound model, as solutions for gaze estimation for remote low resolution systems. Since this work considers head position essential to improve gaze accuracy, a method for head pose estimation is also proposed. The methods are validated in an optimal framework, I2Head database, which combines head and gaze data. The experimental validation of the models demonstrates their sensitivity to image processing inaccuracies, critical in the case of the geometrical model. Static and extreme movement scenarios are analyzed showing the higher robustness of compound and geometrical models in the presence of user’s displacement. Accuracy values of about 3° have been obtained, increasing to values close to 5° in extreme displacement settings, results fully comparable with the state-of-the-art. [ABSTRACT FROM AUTHOR]

Abstract: Lately eye tracking system development and applications are becoming increasingly interesting. Efforts in eye tracking research cover a broad spectrum of fields, being mathematical modeling an important aspect and probably one of the least explored topics. In order to build up a robust and efficient model a deep mathematical review of the geometry and the intrinsic nature of eye tracking systems is needed. Video-oculography represents one of the most popular eye tracking methods due to its non-intrusive nature. The images acquired from user eye are analyzed in order to identify some selected features, and through a calibration process the parameters of a model are adjusted for each user. This paper tries to accomplish the first step of a more extensive model and presents a simple expression for pupil orientation based on framework physical parameters. The proposed model requires alternative calibration strategies depending on the number of parameters employed to obtain an efficient behavior. It exhibits lower errors than other generic mathematical expressions, which normally need more calibration points to construct a competent model. The paper starts modeling a whole video-oculographic system and once the model is derived, the work addresses different simplification ways in order to obtain a simpler and more efficient form. Lastly an experimental validation is provided. [Copyright &y& Elsevier]