The gaze estimation in the mobile scenario often suffers from the extrapolation and parallax errors. In this paper, we propose a novel calibration framework to achieve the precise gaze estimation for head-mounted gaze trackers. Our proposed framework consists of two steps to learn a point-to-point and a point-to-line relations, respectively. The aim of step I is to infer the relation between pupil centers and spatially constrained points of regard. By adopting the 'CalibMe' gaze data acquisition method, a sparse Gaussian Process using pseudo-inputs is used to capture the smooth residual field unmodeled by the polynomial function. Meanwhile, a distraction detection method is introduced to identify the moment when user's attention is taken away from the calibration point thereby removing outliers. By combining with the point-to-point relation inferred in step I, the observed parallax errors are leveraged in step II to obtain a point-to-line relation, i.e., each pupil center will correspond to an epipolar line. Thus, the real image gaze point projected from different depths is predicted as the intersection of two epipolar lines inferred from binocular data. The simulation and experimental results show the effectiveness of our proposed calibration framework for head-mounted gaze trackers.