TabletGaze: Dataset and Algorithm for Unconstrained Appearance-based Gaze Estimation in Mobile Tablets

In this thesis, 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 baseline 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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Engineering

Grantor

Rice University

Year dc:date.issued

2015

Author dc:creator

• Huang, Qiong

Advisor dc:contributor.advisor

• Sabharwal, Ashutosh

Committee members dc:contributor.committeemember

• Veeraraghavan, Ashok
• Kemere, Caleb

• Eyes
• Gaze Tracking/Estimation
• Dataset
• Pattern Recognition
• Computer Vision
• Mobile Device/Tablet

Statement dc:rights

• Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1911/88352

OAI identifier oai:identifier

oai:repository.rice.edu:1911/88352