Rendering of Eyes for Eye-Shape Registration and Gaze Estimation

Erroll Wood, Tadas Baltrusaitis, Xucong Zhang, Yusuke Sugano, Peter Robinson, Andreas Bulling; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3756-3764

Abstract


Images of the eye are key in several computer vision problems, such as shape registration and gaze estimation. Recent large-scale supervised methods for these problems require time-consuming data collection and manual annotation, which can be unreliable. We propose synthesizing perfectly labelled photo-realistic training data in a fraction of the time. We used computer graphics techniques to build a collection of dynamic eye-region models from head scan geometry. These were randomly posed to synthesize close-up eye images for a wide range of head poses, gaze directions, and illumination conditions. We used our model's controllability to verify the importance of realistic illumination and shape variations in eye-region training data. Finally, we demonstrate the benefits of our synthesized training data (SynthesEyes) by out-performing state-of-the-art methods for eye-shape registration as well as cross-dataset appearance-based gaze estimation in the wild.

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[bibtex]
@InProceedings{Wood_2015_ICCV,
author = {Wood, Erroll and Baltrusaitis, Tadas and Zhang, Xucong and Sugano, Yusuke and Robinson, Peter and Bulling, Andreas},
title = {Rendering of Eyes for Eye-Shape Registration and Gaze Estimation},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
}