  Recent studies on appearance based gaze estimation indicate the ability of
Neural Networks to decode gaze information from facial images encompassing pose
information. In this paper, we propose Gaze-Net: A capsule network capable of
decoding, representing, and estimating gaze information from ocular region
images. We evaluate our proposed system using two publicly available datasets,
MPIIGaze (200,000+ images in the wild) and Columbia Gaze (5000+ images of users
with 21 gaze directions observed at 5 camera angles/positions). Our model
achieves a Mean Absolute Error (MAE) of 2.84$^\circ$ for Combined angle error
estimate within dataset for MPI-IGaze dataset. Further, model achieves a MAE of
10.04$^\circ$ for across dataset gaze estimation error for Columbia gaze
dataset. Through transfer learning, the error is reduced to 5.9$^\circ$. The
results show this approach is promising with implications towards using
commodity webcams to develop low-cost multi-user gaze tracking systems.
