Skip to content
Back to outputs

SDM-based means of gradient for eye center localization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Standard

SDM-based means of gradient for eye center localization. / Xia, Yifan; Lou, Jianwen; Dong, Junyu; Li, Gongfa; Yu, Hui.

2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, 2018. p. 862-867.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Harvard

Xia, Y, Lou, J, Dong, J, Li, G & Yu, H 2018, SDM-based means of gradient for eye center localization. in 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, pp. 862-867, 16th IEEE International Conference on Pervasive Intelligence, 12/08/18. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17

APA

Xia, Y., Lou, J., Dong, J., Li, G., & Yu, H. (2018). SDM-based means of gradient for eye center localization. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (pp. 862-867). IEEE. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17

Vancouver

Xia Y, Lou J, Dong J, Li G, Yu H. SDM-based means of gradient for eye center localization. In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE. 2018. p. 862-867 https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17

Author

Xia, Yifan ; Lou, Jianwen ; Dong, Junyu ; Li, Gongfa ; Yu, Hui. / SDM-based means of gradient for eye center localization. 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, 2018. pp. 862-867

Bibtex

@inproceedings{169b4187e47c46689893566974c0d21c,
title = "SDM-based means of gradient for eye center localization",
abstract = "For eye gaze estimation and eye tracking, localizing eye center is a crucial requirement. This task is challenging work because of the significant variability of eye appearance in illumination, shape, color and viewing angles. In this paper, we improve the performance of means of gradient method in low resolution images, which could locate the eye center more accurately. The proposed method applies Supervised Descent Method (SDM), which has remarkable achievement in the field of face align-ment, to improve the traditional means of gradient method in localizing eye center. We extensively evaluate our method on BioID database which is very challenging and realistic for eye center localization. Moreover, we have compared our method with existing state of the art methods and the results of the experiment confirm that the proposed method is an attractive alternative for eye center localization.",
keywords = "RCUK, EPSRC, EP/N025849/1",
author = "Yifan Xia and Jianwen Lou and Junyu Dong and Gongfa Li and Hui Yu",
year = "2018",
month = oct,
day = "29",
doi = "10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17",
language = "English",
isbn = "978-1-5386-7519-9",
pages = "862--867",
booktitle = "2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech)",
publisher = "IEEE",
note = "16th IEEE International Conference on Pervasive Intelligence ; Conference date: 12-08-2018 Through 15-08-2018",

}

RIS

TY - GEN

T1 - SDM-based means of gradient for eye center localization

AU - Xia, Yifan

AU - Lou, Jianwen

AU - Dong, Junyu

AU - Li, Gongfa

AU - Yu, Hui

PY - 2018/10/29

Y1 - 2018/10/29

N2 - For eye gaze estimation and eye tracking, localizing eye center is a crucial requirement. This task is challenging work because of the significant variability of eye appearance in illumination, shape, color and viewing angles. In this paper, we improve the performance of means of gradient method in low resolution images, which could locate the eye center more accurately. The proposed method applies Supervised Descent Method (SDM), which has remarkable achievement in the field of face align-ment, to improve the traditional means of gradient method in localizing eye center. We extensively evaluate our method on BioID database which is very challenging and realistic for eye center localization. Moreover, we have compared our method with existing state of the art methods and the results of the experiment confirm that the proposed method is an attractive alternative for eye center localization.

AB - For eye gaze estimation and eye tracking, localizing eye center is a crucial requirement. This task is challenging work because of the significant variability of eye appearance in illumination, shape, color and viewing angles. In this paper, we improve the performance of means of gradient method in low resolution images, which could locate the eye center more accurately. The proposed method applies Supervised Descent Method (SDM), which has remarkable achievement in the field of face align-ment, to improve the traditional means of gradient method in localizing eye center. We extensively evaluate our method on BioID database which is very challenging and realistic for eye center localization. Moreover, we have compared our method with existing state of the art methods and the results of the experiment confirm that the proposed method is an attractive alternative for eye center localization.

KW - RCUK

KW - EPSRC

KW - EP/N025849/1

U2 - 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17

DO - 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17

M3 - Conference contribution

SN - 978-1-5386-7519-9

SP - 862

EP - 867

BT - 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech)

PB - IEEE

T2 - 16th IEEE International Conference on Pervasive Intelligence

Y2 - 12 August 2018 through 15 August 2018

ER -

ID: 10937803