It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation
Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling
Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2299-2308, 2017.
Abstract
Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and par- ticularly pronounced for the most challenging extreme head poses.Links
Paper: zhang17_cvprw.pdf
BibTeX
@inproceedings{zhang17_cvprw,
title = {It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation},
author = {Zhang, Xucong and Sugano, Yusuke and Fritz, Mario and Bulling, Andreas},
booktitle = {Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
year = {2017},
doi = {10.1109/CVPRW.2017.284},
pages = {2299-2308}
}
