Learning-by-Synthesis for Appearance-Based 3D Gaze Estimation

@article{Sugano2014LearningbySynthesisFA,
  title={Learning-by-Synthesis for Appearance-Based 3D Gaze Estimation},
  author={Yusuke Sugano and Yasuyuki Matsushita and Yoichi Sato},
  journal={2014 IEEE Conference on Computer Vision and Pattern Recognition},
  year={2014},
  pages={1821-1828}
}
Inferring human gaze from low-resolution eye images is still a challenging task despite its practical importance in many application scenarios. This paper presents a learning-by-synthesis approach to accurate image-based gaze estimation that is person- and head pose-independent. Unlike existing appearance-based methods that assume person-specific training data, we use a large amount of cross-subject training data to train a 3D gaze estimator. We collect the largest and fully calibrated multi… CONTINUE READING
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