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A Bayesian Framework for Accurate Eye Center Localization

  • Zhou Liu
  • Heng Yang
  • Ming Dong
  • Jing Hua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8888)

Abstract

Accurate localization of eye centers is very important in many computer vision applications. In this paper, we present a novel hybrid method for accurate eye center localization, in which the global appearance, the local features and the temporal information through eye tracking are fused under the Bayesian framework. Specifically, we first construct the position prior to incorporate the global appearance information, which makes our approach robust for images or videos with low resolutions. Then, the likelihood function is built based on local features in the eye region. Finally, after fusing the temporal information provided by eye tracking, we obtain the posterior distribution, and the mean shift method is used to find the locations of the eye centers. Our extensive experimental results on public datasets demonstrate that our system is robust to the variations of illumination and head pose, and outperforms several state-of-the-art methods.

Keywords

Posterior Distribution Temporal Information Bayesian Framework Shift Method Computer Vision Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Zhou Liu
    • 1
  • Heng Yang
    • 1
  • Ming Dong
    • 2
  • Jing Hua
    • 2
  1. 1.BeiJing Innovisgroup CompanyBeijingChina
  2. 2.Department of Computer ScienceWayne State UniversityDetroitUSA

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