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arXiv · 1805.04771

Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings

Seonwook Park·Xucong Zhang·Andreas Bulling·Otmar Hilliges
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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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Seonwook Park, Xucong Zhang, Andreas Bulling, Otmar Hilliges. 2018-05-12. Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings. https://doi.org/10.1145/3204493.3204545

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