SDM-based means of gradient for eye center localization

Yifan Xia; Jianwen Lou; Junyu Dong; Gongfa Li; Hui Yu

doi:10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17

SDM-based means of gradient for eye center localization

Yifan Xia, Jianwen Lou, Junyu Dong, Gongfa Li, Hui Yu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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.

Original language	English
Title of host publication	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	Institute of Electrical and Electronics Engineers Inc.
Pages	862-867
Number of pages	6
ISBN (Electronic)	978-1-5386-7518-2
ISBN (Print)	978-1-5386-7519-9
DOIs	https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17
Publication status	Published - 29 Oct 2018
Event	16th IEEE International Conference on Pervasive Intelligence - Duration: 12 Aug 2018 → 15 Aug 2018

Conference

Conference	16th IEEE International Conference on Pervasive Intelligence
Period	12/08/18 → 15/08/18

Keywords

RCUK
EPSRC
EP/N025849/1

Access to Document

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

SDM-based means of gradient for eye center localization
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Accepted author manuscript (Post-print), 568 KB

Cite this

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). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17

Xia, Yifan ; Lou, Jianwen ; Dong, Junyu et al. / 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). Institute of Electrical and Electronics Engineers Inc., 2018. pp. 862-867

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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,

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doi = "10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17",

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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)",

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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). Institute of Electrical and Electronics Engineers Inc., 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

SDM-based means of gradient for eye center localization. / Xia, Yifan; Lou, Jianwen; Dong, Junyu et al.
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). Institute of Electrical and Electronics Engineers Inc., 2018. p. 862-867.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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

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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 - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE International Conference on Pervasive Intelligence

Y2 - 12 August 2018 through 15 August 2018

ER -

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). Institute of Electrical and Electronics Engineers Inc. 2018. p. 862-867 doi: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00-17

SDM-based means of gradient for eye center localization

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