  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 particularly pronounced for the most challenging extreme head
poses.
