  Automatic eye gaze estimation has interested researchers for a while now. In
this paper, we propose an unsupervised learning based method for estimating the
eye gaze region. To train the proposed network "Ize-Net" in self-supervised
manner, we collect a large `in the wild' dataset containing 1,54,251 images
from the web. For the images in the database, we divide the gaze into three
regions based on an automatic technique based on pupil-centers localization and
then use a feature-based technique to determine the gaze region. The
performance is evaluated on the Tablet Gaze and CAVE datasets by fine-tuning
results of Ize-Net for the task of eye gaze estimation. The feature
representation learned is also used to train traditional machine learning
algorithms for eye gaze estimation. The results demonstrate that the proposed
method learns a rich data representation, which can be efficiently fine-tuned
for any eye gaze estimation dataset.
