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

TabletGaze: dataset and analysis for unconstrained appearance-based gaze estimation in mobile... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Vision and Applications Springer Journals

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

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany
Subject
Computer Science; Pattern Recognition; Image Processing and Computer Vision; Communications Engineering, Networks
ISSN
0932-8092
eISSN
1432-1769
D.O.I.
10.1007/s00138-017-0852-4
Publisher site
See Article on Publisher Site

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 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.

Journal

Machine Vision and ApplicationsSpringer Journals

Published: Jul 7, 2017

References

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