TabletGaze: Unconstrained Appearance-based Gaze Estimation in Mobile Tablets

TL;DR

This paper introduces TabletGaze, a new approach for unconstrained, appearance-based gaze estimation on mobile tablets, utilizing a large diverse dataset and a novel algorithm to achieve accurate results during natural use.

Contribution

The paper presents the first large unconstrained tablet gaze dataset and a new gaze estimation algorithm that works without calibration in natural tablet usage scenarios.

Findings

Mean gaze estimation error of 3.17 cm.

Factors like race, glasses, and posture significantly affect accuracy.

The dataset includes diverse subjects with different postures and gaze points.

Abstract

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.