Learning-by-Synthesis for Appearance-Based 3D Gaze Estimation


Inferring human gaze from low-resolution eye images is still a challenging task despite its practical importance in many application scenarios. This paper presents a learning-by-synthesis approach to accurate image-based gaze estimation that is person- and head pose-independent. Unlike existing appearance-based methods that assume person-specific training data, we use a large amount of cross-subject training data to train a 3D gaze estimator. We collect the largest and fully calibrated multi-view gaze dataset and perform a 3D reconstruction in order to generate dense training data of eye images. By using the synthesized dataset to learn a random regression forest, we show that our method outperforms existing methods that use low-resolution eye images.

DOI: 10.1109/CVPR.2014.235

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@article{Sugano2014LearningbySynthesisFA, title={Learning-by-Synthesis for Appearance-Based 3D Gaze Estimation}, author={Yusuke Sugano and Yasuyuki Matsushita and Yoichi Sato}, journal={2014 IEEE Conference on Computer Vision and Pattern Recognition}, year={2014}, pages={1821-1828} }