Our architecture estimates eye region landmarks with a stacked-hourglass network trained on synthetic data (UnityEyes), evaluating directly on eye images taken in unconstrained real-world settings. The landmark coordinates can directly be used for model or feature-based gaze estimation.
Abstract
Conventional feature-based and model-based gaze estimation methods have proven to perform well in settings with controlled illumination and specialized cameras. In unconstrained real-world settings, however, such methods are surpassed by recent appearance-based methods due to difficulties in modeling factors such as illumination changes and other visual artifacts. We present a novel learning-based method for eye region landmark localization that enables conventional methods to be competitive to latest appearance-based methods. Despite having been trained exclusively on synthetic data, our method exceeds the state of the art for iris localization and eye shape registration on real-world imagery. We then use the detected landmarks as input to iterative model-fitting and lightweight learning-based gaze estimation methods. Our approach outperforms existing model-fitting and appearance-based methods in the context of person-independent and personalized gaze estimation.
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Acknowledgments
We would like to thank Erroll Wood, Tadas Baltrusaitis, and Wolfgang Fuhl for their help. This work was supported in part by ERC Grant OPTINT (StG-2016-717054), the Cluster of Excellence on Multimodal Computing and Interaction at Saarland University, Germany, and a JST CREST research grant (JPMJCR14E1), Japan.
Published at
ACM Symposium on Eye Tracking Research and Applications (ETRA), 2018
Best Presentation Award
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