  The study of human gaze behavior in natural contexts requires algorithms for
gaze estimation that are robust to a wide range of imaging conditions. However,
algorithms often fail to identify features such as the iris and pupil centroid
in the presence of reflective artifacts and occlusions. Previous work has shown
that convolutional networks excel at extracting gaze features despite the
presence of such artifacts. However, these networks often perform poorly on
data unseen during training. This work follows the intuition that jointly
training a convolutional network with multiple datasets learns a generalized
representation of eye parts. We compare the performance of a single model
trained with multiple datasets against a pool of models trained on individual
datasets. Results indicate that models tested on datasets in which eye images
exhibit higher appearance variability benefit from multiset training. In
contrast, dataset-specific models generalize better onto eye images with lower
appearance variability.
