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Rendering of eyes for eye-shape registration and gaze estimation

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Wood, E 
Baltruaitis, T 
Zhang, X 
Sugano, Y 

Abstract

© 2015 IEEE. 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.

Description

Keywords

cs.CV, cs.CV

Journal Title

Proceedings of the IEEE International Conference on Computer Vision

Conference Name

Journal ISSN

1550-5499

Volume Title

2015

Publisher

IEEE
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