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We study gaze estimation on tablets, our key design goal is uncalibrated gazeestimation using the front-facing camera during natural use of tablets, wherethe posture and method of holding the tablet is not constrained. We collectedthe first large unconstrained gaze dataset of tablet users, labeled RiceTabletGaze dataset. The dataset consists of 51 subjects, each with 4 differentpostures and 35 gaze locations. Subjects vary in race, gender and in their needfor prescription glasses, all of which might impact gaze estimation accuracy.Driven by our observations on the collected data, we present a TabletGazealgorithm for automatic gaze estimation using multi-level HoG feature andRandom Forests regressor. The TabletGaze algorithm achieves a mean error of3.17 cm. We perform extensive evaluation on the impact of various factors suchas dataset size, race, wearing glasses and user posture on the gaze estimationaccuracy and make important observations about the impact of these factors.

TabletGaze: Unconstrained Appearance-based Gaze Estimation in Mobile Tablets