Where Are You Looking At? - Feature-Based Eye Tracking on Unmodified Tablets

This paper introduces our work towards implementing eye tracking on commodity devices. We describe our feature-based approach and the eye tracking system working on a commodity tablet. We recorded the data of 5 subjects following an animation on screen as reference. On the assumption that the position of device and user´s head is stable, the average distance error between estimated gaze point to actual gaze point is around 12.23 [mm] using user-dependent training.