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Optical Engineering

Gaze tracking based on active appearance model and multiple support vector regression on mobile devices
Author(s): Eui Chul Lee; You Jin Ko; Kang Ryoung Park
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Paper Abstract

Gaze tracking technology is a convenient interfacing method for mobile devices. Most previous studies used a large-sized desktop or head-mounted display. In this study, we propose a novel gaze tracking method using an active appearance model (AAM) and multiple support vector regression (SVR) on a mobile device. Our research has four main contributions. First, in calculating the gaze position, the amount of facial rotation and translation based on four feature values is computed using facial feature points detected by AAM. Second, the amount of eye rotation based on two feature values is computed for measuring eye gaze position. Third, to compensate for the fitting error of an AAM in facial rotation, we use the adaptive discrete Kalman filter (DKF), which applies a different velocity of state transition matrix to the facial feature points. Fourth, we obtain gaze position on a mobile device based on multiple SVR by separating the rotation and translation of face and eye rotation. Experimental results show that the root mean square (rms) gaze error is 36.94 pixels on the 4.5-in. screen of a mobile device with a screen resolution of 800×600 pixels.

Paper Details

Date Published: 1 July 2009
PDF: 11 pages
Opt. Eng. 48(7) 077002 doi: 10.1117/1.3158992
Published in: Optical Engineering Volume 48, Issue 7
Show Author Affiliations
Eui Chul Lee, Sangmyung Univ. (Korea, Republic of)
You Jin Ko, Sangmyung Univ. (Korea, Republic of)
Kang Ryoung Park, Dongguk Univ. (Korea, Republic of)


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