%0 Conference Paper %1 zhang17_cvprw %A Zhang, Xucong %A Sugano, Yusuke %A Fritz, Mario %A Bulling, Andreas %B Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) %D 2017 %K %P 2299-2308 %R 10.1109/CVPRW.2017.284 %T It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation %X 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.

@inproceedings{zhang17_cvprw, 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.}, added-at = {2024-08-16T14:47:54.000+0200}, author = {Zhang, Xucong and Sugano, Yusuke and Fritz, Mario and Bulling, Andreas}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2ea199907e0ee25099bf5d485b146f1fe/hcics}, booktitle = {Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, doi = {10.1109/CVPRW.2017.284}, interhash = {3166d864f45a6c2a6403b87d54fc7311}, intrahash = {ea199907e0ee25099bf5d485b146f1fe}, keywords = {}, note = {spotlight}, pages = {2299-2308}, timestamp = {2024-08-16T12:47:54.000+0200}, title = {It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation}, year = 2017 }