Development of precise mobile gaze tracking system based on online sparse Gaussian process regression and smooth-pursuit identification

In this paper, we aim to address two challenges in the implementation of mobile gaze tracking systems, i.e., the parallax error and the inflexible calibration procedure. Our proposed method mainly involves two steps and all the calibration process can be completed without needs to receive user's commands. At first, instead of fixating at calibration points successively, users are required to fixate at one calibration point while smoothly and persistently varying the head position. In this case, the eye movements will compensate for head movements due to the activation of the smooth pursuit system. Then the PCA analysis is applied for distinguishing the smooth pursuits from other kinds of eye movements to get reliable training data. Meanwhile, a online sparse Gaussian Process using the FITC approximation is proposed to model the relationship between image pupil centers and gaze points. The next step aims to compensate the parallax error by recovering the epipolar geometry of gaze tracking systems and eyeballs. Users are asked to fixate at the points with different distances and detected parallax errors can be applied to estimate the epipolar geometry of mobile gaze trackers by solving a nonlinear optimization problem. Thus the real image gaze point with different depths can be straightforwardly estimated as the intersection point of two epipolar lines derived from binocular data. The simulation and experimental results demonstrate the effectiveness of our proposed method.