  Estimating human gaze from natural eye images only is a challenging task.
Gaze direction can be defined by the pupil- and the eyeball center where the
latter is unobservable in 2D images. Hence, achieving highly accurate gaze
estimates is an ill-posed problem. In this paper, we introduce a novel deep
neural network architecture specifically designed for the task of gaze
estimation from single eye input. Instead of directly regressing two angles for
the pitch and yaw of the eyeball, we regress to an intermediate pictorial
representation which in turn simplifies the task of 3D gaze direction
estimation. Our quantitative and qualitative results show that our approach
achieves higher accuracies than the state-of-the-art and is robust to variation
in gaze, head pose and image quality.
