TabletGaze: dataset and analysis for unconstrained appearance-based gaze estimation in mobile tablets

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 are not constrained. We collected a 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. We made three major observations on the collected data and employed a baseline algorithm for analyzing the impact of several factors on gaze estimation accuracy. The baseline algorithm is based on multilevel HoG feature and Random Forests regressor, which achieves a mean error of 3.17 cm. We perform extensive evaluation on the impact of various practical factors such as person dependency, dataset size, race, wearing glasses and user posture on the gaze estimation accuracy.