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2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Boston, MA, USA
June 7, 2015 to June 12, 2015
ISSN: 1063-6919
ISBN: 978-1-4673-6963-3
pp: 4511-4520
Xucong Zhang , Perceptual User Interfaces Group, Max Planck Institute for Informatics, Saarbrücken, Germany
Yusuke Sugano , Perceptual User Interfaces Group, Max Planck Institute for Informatics, Saarbrücken, Germany
Mario Fritz , Scalable Learning and Perception Group, Max Planck Institute for Informatics, Saarbrücken, Germany
Andreas Bulling , Perceptual User Interfaces Group, Max Planck Institute for Informatics, Saarbrücken, Germany
ABSTRACT
Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing ones with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks that significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithms on three current datasets, including our own. This evaluation provides clear insights and allows us to identify key research challenges of gaze estimation in the wild.
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CITATION

X. Zhang, Y. Sugano, M. Fritz and A. Bulling, "Appearance-based gaze estimation in the wild," 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 2015, pp. 4511-4520.
doi:10.1109/CVPR.2015.7299081
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