2015/08/05 by Qiong Huang, Huang, Qiong, Ashok Veeraraghavan +3 · 2 citations
Computer Science · Medicine · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Hand Gesture Recognition Systems #Retinal Imaging and Analysis #cs.CV
paper · pdf · doi:10.48550/arxiv.1508.01244
18 pages, 17 figures, submitted to journal, website hosting the dataset: http://sh.rice.edu/tablet_gaze.html
openalex publication_date 2015/08/05 · openalex created_date 2016/06/24 · arxiv created 2016/07/16 · arxiv updated 2016/07/19 · openalex updated_date 2026/07/28
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.