TabletGaze: Unconstrained Appearance-based Gaze Estimation in Mobile Tablets
This addresses gaze estimation for tablet users in unconstrained settings, but it is incremental as it applies existing methods to a new dataset.
The researchers tackled uncalibrated gaze estimation on tablets using the front-facing camera during natural use, achieving a mean error of 3.17 cm with their TabletGaze algorithm based on multi-level HoG features and Random Forests regressor.
We study gaze estimation on tablets, our key design goal is uncalibrated gaze estimation using the front-facing camera during natural use of tablets, where the posture and method of holding the tablet is not constrained. We collected the first large unconstrained gaze dataset of tablet users, labeled Rice TabletGaze dataset. The dataset consists of 51 subjects, each with 4 different postures and 35 gaze locations. Subjects vary in race, gender and in their need for prescription glasses, all of which might impact gaze estimation accuracy. Driven by our observations on the collected data, we present a TabletGaze algorithm for automatic gaze estimation using multi-level HoG feature and Random Forests regressor. The TabletGaze algorithm achieves a mean error of 3.17 cm. We perform extensive evaluation on the impact of various factors such as dataset size, race, wearing glasses and user posture on the gaze estimation accuracy and make important observations about the impact of these factors.