A Bayesian Framework for Accurate Eye Center Localization
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.
KeywordsPosterior Distribution Temporal Information Bayesian Framework Shift Method Computer Vision Application
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