Eyegaze Detection from Monocular Camera Image for Eyegaze Communication System

Ryo OHTERA; Takahiko HORIUCHI; Hiroaki KOTERA

doi:10.1587/transinf.E93.D.134

Eyegaze Detection from Monocular Camera Image for Eyegaze Communication System

Ryo OHTERA, Takahiko HORIUCHI, Hiroaki KOTERA

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An eyegaze interface is one of the key technologies as an input device in the ubiquitous-computing society. In particular, an eyegaze communication system is very important and useful for severely handicapped users such as quadriplegic patients. Most of the conventional eyegaze tracking algorithms require specific light sources, equipment and devices. In this study, a simple eyegaze detection algorithm is proposed using a single monocular video camera. The proposed algorithm works under the condition of fixed head pose, but slight movement of the face is accepted. In our system, we assume that all users have the same eyeball size based on physiological eyeball models. However, we succeed to calibrate the physiologic movement of the eyeball center depending on the gazing direction by approximating it as a change in the eyeball radius. In the gaze detection stage, the iris is extracted from a captured face frame by using the Hough transform. Then, the eyegaze angle is derived by calculating the Euclidean distance of the iris centers between the extracted frame and a reference frame captured in the calibration process. We apply our system to an eyegaze communication interface, and verified the performance through key typing experiments with a visual keyboard on display.

Publication: IEICE TRANSACTIONS on Information Vol.E93-D No.1 pp.134-143

Publication Date: 2010/01/01

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Online ISSN: 1745-1361

DOI: 10.1587/transinf.E93.D.134

Type of Manuscript: PAPER

Category: Image Recognition, Computer Vision

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Ryo OHTERA, Takahiko HORIUCHI, Hiroaki KOTERA, "Eyegaze Detection from Monocular Camera Image for Eyegaze Communication System" in IEICE TRANSACTIONS on Information, vol. E93-D, no. 1, pp. 134-143, January 2010, doi: 10.1587/transinf.E93.D.134.
Abstract: An eyegaze interface is one of the key technologies as an input device in the ubiquitous-computing society. In particular, an eyegaze communication system is very important and useful for severely handicapped users such as quadriplegic patients. Most of the conventional eyegaze tracking algorithms require specific light sources, equipment and devices. In this study, a simple eyegaze detection algorithm is proposed using a single monocular video camera. The proposed algorithm works under the condition of fixed head pose, but slight movement of the face is accepted. In our system, we assume that all users have the same eyeball size based on physiological eyeball models. However, we succeed to calibrate the physiologic movement of the eyeball center depending on the gazing direction by approximating it as a change in the eyeball radius. In the gaze detection stage, the iris is extracted from a captured face frame by using the Hough transform. Then, the eyegaze angle is derived by calculating the Euclidean distance of the iris centers between the extracted frame and a reference frame captured in the calibration process. We apply our system to an eyegaze communication interface, and verified the performance through key typing experiments with a visual keyboard on display.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.134/_p

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@ARTICLE{e93-d_1_134,
author={Ryo OHTERA, Takahiko HORIUCHI, Hiroaki KOTERA, },
journal={IEICE TRANSACTIONS on Information},
title={Eyegaze Detection from Monocular Camera Image for Eyegaze Communication System},
year={2010},
volume={E93-D},
number={1},
pages={134-143},
abstract={An eyegaze interface is one of the key technologies as an input device in the ubiquitous-computing society. In particular, an eyegaze communication system is very important and useful for severely handicapped users such as quadriplegic patients. Most of the conventional eyegaze tracking algorithms require specific light sources, equipment and devices. In this study, a simple eyegaze detection algorithm is proposed using a single monocular video camera. The proposed algorithm works under the condition of fixed head pose, but slight movement of the face is accepted. In our system, we assume that all users have the same eyeball size based on physiological eyeball models. However, we succeed to calibrate the physiologic movement of the eyeball center depending on the gazing direction by approximating it as a change in the eyeball radius. In the gaze detection stage, the iris is extracted from a captured face frame by using the Hough transform. Then, the eyegaze angle is derived by calculating the Euclidean distance of the iris centers between the extracted frame and a reference frame captured in the calibration process. We apply our system to an eyegaze communication interface, and verified the performance through key typing experiments with a visual keyboard on display.},
keywords={},
doi={10.1587/transinf.E93.D.134},
ISSN={1745-1361},
month={January},}

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TY - JOUR
TI - Eyegaze Detection from Monocular Camera Image for Eyegaze Communication System
T2 - IEICE TRANSACTIONS on Information
SP - 134
EP - 143
AU - Ryo OHTERA
AU - Takahiko HORIUCHI
AU - Hiroaki KOTERA
PY - 2010
DO - 10.1587/transinf.E93.D.134
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2010
AB - An eyegaze interface is one of the key technologies as an input device in the ubiquitous-computing society. In particular, an eyegaze communication system is very important and useful for severely handicapped users such as quadriplegic patients. Most of the conventional eyegaze tracking algorithms require specific light sources, equipment and devices. In this study, a simple eyegaze detection algorithm is proposed using a single monocular video camera. The proposed algorithm works under the condition of fixed head pose, but slight movement of the face is accepted. In our system, we assume that all users have the same eyeball size based on physiological eyeball models. However, we succeed to calibrate the physiologic movement of the eyeball center depending on the gazing direction by approximating it as a change in the eyeball radius. In the gaze detection stage, the iris is extracted from a captured face frame by using the Hough transform. Then, the eyegaze angle is derived by calculating the Euclidean distance of the iris centers between the extracted frame and a reference frame captured in the calibration process. We apply our system to an eyegaze communication interface, and verified the performance through key typing experiments with a visual keyboard on display.
ER -