(IEEE, 2020) Martinikorena Aranburu, Ion; Larumbe Bergera, Andoni; Ariz Galilea, Mikel; Porta Cuéllar, Sonia; Cabeza Laguna, Rafael; Villanueva Larre, Arantxa; Ingeniería Eléctrica, Electrónica y de Comunicación; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren
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.
