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 proceedingConference 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 languageEnglish
Title of host publication2018 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)
PublisherIEEE
Pages862-867
Number of pages6
ISBN (Electronic)978-1-5386-7518-2
ISBN (Print)978-1-5386-7519-9
DOIs
Publication statusPublished - 29 Oct 2018
Event16th IEEE International Conference on Pervasive Intelligence -
Duration: 12 Aug 201815 Aug 2018

Conference

Conference16th IEEE International Conference on Pervasive Intelligence
Period12/08/1815/08/18

Keywords

  • RCUK
  • EPSRC
  • EP/N025849/1

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