Gaze-estimation for consumer-grade cameras using a Gaussian process latent variable model

Abstract

Commercial gaze-tracking devices provide accurate measurements of the visual gaze and are applied to a broad range of problems in marketing, human-computer interaction, and health care technology. In some applications commercial systems are either unavailable or unaffordable. Therefore, developing low cost solutions using off the shelf components is worthwhile. In the paper at hand, we apply a hierarchy of Gaussian processes, a class of probabilistic function regressors, to the problem of visual gaze-tracking for consumer grade cameras. Gaussian process latent variable models lead to a lower dimensional manifold which represents the gaze space. Finally, a Gaussian process mapping from screen coordinates to gaze manifold enables us to seek for the users visual gaze point given a previously unseen eye-patch. In our experiments, we achieve mean errors of approximately 2 cm for a consumer grade webcam that is positioned 30-40 cm in front of the user.