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 [BibTeX] [Marc21]
Geometric Generative Gaze Estimation (G3E) for Remote RGB-D Cameras
Type of publication: Conference paper
Citation: FunesMora_CVPR_2014
Booktitle: IEEE Computer Vision and Pattern Recognition Conference
Year: 2014
Month: June
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.
Keywords: Gaze estimation, generative models, geometric method, remote, RGB-D, segmentation, variational inference
Projects Idiap
Authors Funes Mora, Kenneth Alberto
Odobez, Jean-Marc
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Total mark: 0
  • FunesMora_CVPR_2014.pdf