  Unconstrained remote gaze tracking using off-the-shelf cameras is a
challenging problem. Recently, promising algorithms for appearance-based gaze
estimation using convolutional neural networks (CNN) have been proposed.
Improving their robustness to various confounding factors including variable
head pose, subject identity, illumination and image quality remain open
problems. In this work, we study the effect of variable head pose on machine
learning regressors trained to estimate gaze direction. We propose a novel
branched CNN architecture that improves the robustness of gaze classifiers to
variable head pose, without increasing computational cost. We also present
various procedures to effectively train our gaze network including transfer
learning from the more closely related task of object viewpoint estimation and
from a large high-fidelity synthetic gaze dataset, which enable our ten times
faster gaze network to achieve competitive accuracy to its current
state-of-the-art direct competitor.
