Fig. 2

Results for one subject in our study (Subject 8). Crosses mark iTracker’s predictions. Points in matching colour indicate the true gaze locations for those predictions. Shaded areas represent the phone screen, and for Task 2 and 3 also the outline of the eyes and mouth. a–c Gaze estimates for Task 1. d–f Estimates for Task 2. g–i Gaze estimates for Task 3. j–l Estimates for Task 4. In the top row (a, d, g, j), the raw output of iTracker is shown. The middle and bottom row of the panel show these predictions corrected using either a SVR-based (b, e, h, k) or a linear transformation-based calibration method (c, f, i, l). Overall, iTracker manages to capture the true underlying pattern, although it appears shifted and scaled with respect to the reference (a, d, g, j). Calibration can rectify this, resulting in good overlap between true and estimated gaze positions (middle and bottom row; see also Fig. 3). Moreover, we find that the simple linear transformation performs better than the SVR-based method (compare middle and bottom row)