The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Uma interface eyegaze é uma das principais tecnologias como dispositivo de entrada na sociedade da computação onipresente. Em particular, um sistema de comunicação ocular é muito importante e útil para utilizadores com deficiências graves, tais como pacientes tetraplégicos. A maioria dos algoritmos convencionais de rastreamento do olhar ocular requerem fontes de luz, equipamentos e dispositivos específicos. Neste estudo, um algoritmo simples de detecção do olhar ocular é proposto usando uma única câmera de vídeo monocular. O algoritmo proposto funciona sob a condição de postura fixa da cabeça, mas leves movimentos da face são aceitos. Em nosso sistema, assumimos que todos os usuários têm o mesmo tamanho de globo ocular com base em modelos fisiológicos de globo ocular. No entanto, conseguimos calibrar o movimento fisiológico do centro do globo ocular dependendo da direção do olhar, aproximando-o como uma mudança no raio do globo ocular. Na etapa de detecção do olhar, a íris é extraída de um quadro facial capturado usando a transformada de Hough. Em seguida, o ângulo do olhar é derivado calculando a distância euclidiana dos centros da íris entre o quadro extraído e um quadro de referência capturado no processo de calibração. Aplicamos nosso sistema a uma interface de comunicação eyegaze e verificamos o desempenho por meio de experimentos de digitação de teclas com um teclado visual em exibição.
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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 -