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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol.5(6): 507-511 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2015.5.6.561

Appearance-Based Gaze Estimation for Digital Signage Considering Head Pose

Hiroki Yoshimura, Maiya Hori, Tadaaki Shimizu, and Yoshio Iwai
Abstract—Digital signage often uses a large display with the camera placed on the outer frame and set with a wide angle to capture the face of the viewer. In some cases the viewer’s iris is obstructed due to the gaze position of the viewer. In this study, we present an appearance-based gaze estimation method that can be used for digital signage even when the iris is not fully visible. The proposed approach uses the angle of the head and the image of the eye area as features for a neural network machine learning algorithm. Our subject experiments confirm that we achieve accurate focus-point gaze estimation.

Index Terms—Gaze estimation, digital signage, head pose, neural network.

The authors are with the Department of Information and Electronics, Graduate school of Engineering, Tottori University, 4-101 Koyama-minami, Tottori, Japan (e-mail: yosimura@ike.tottori-u.ac.jp, tadaaki@ike.tottori-u.ac.jp, hori@ike.tottori-u.ac.jp, iwai@ike.tottori-u.ac.jp).

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Cite: Hiroki Yoshimura, Maiya Hori, Tadaaki Shimizu, and Yoshio Iwai, "Appearance-Based Gaze Estimation for Digital Signage Considering Head Pose," International Journal of Machine Learning and Computing vol.5, no. 6, pp. 507-511, 2015.

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