%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{FunesMora_CVPR_2014,
         author = {Funes Mora, Kenneth Alberto and Odobez, Jean-Marc},
       keywords = {Gaze estimation, generative models, geometric method, remote, RGB-D, segmentation, variational inference},
       projects = {Idiap},
          month = jun,
          title = {Geometric Generative Gaze Estimation (G3E) for Remote RGB-D Cameras},
      booktitle = {IEEE Computer Vision and Pattern Recognition Conference},
           year = {2014},
          pages = {1773-1780},
      publisher = {IEEE},
       location = {Columbus, Ohio,USA},
            doi = {10.1109/CVPR.2014.229},
       abstract = {We propose a head pose invariant gaze estimation model for distant RGB-D cameras. It relies  on a geometric understanding of the 3D gaze action and generation of eye images. By introducing a semantic segmentation  of the eye region within a generative process, the model 
(i) avoids the critical feature tracking of geometrical approaches requiring high resolution images; (ii) decouples the person dependent geometry from the ambient conditions, allowing adaptation to different conditions without retraining. Priors  in the generative framework are adequate for training from few samples. In addition, the model is capable of gaze extrapolation allowing for less restrictive training schemes. Comparisons with state of the art methods validate these properties which make our method highly valuable for addressing many diverse tasks in sociology,  HRI and HCI.},
            pdf = {https://publications.idiap.ch/attachments/papers/2014/FunesMora_CVPR_2014.pdf}
}