  Eye contact is a crucial element of non-verbal communication that signifies
interest, attention, and participation in social interactions. As a result,
measures of eye contact arise in a variety of applications such as the
assessment of the social communication skills of children at risk for
developmental disorders such as autism, or the analysis of turn-taking and
social roles during group meetings. However, the automated measurement of
visual attention during naturalistic social interactions is challenging due to
the difficulty of estimating a subject's looking direction from video. This
paper proposes a novel approach to eye contact detection during adult-child
social interactions in which the adult wears a point-of-view camera which
captures an egocentric view of the child's behavior. By analyzing the child's
face regions and inferring their head pose we can accurately identify the onset
and duration of the child's looks to their social partner's eyes. We introduce
the Pose-Implicit CNN, a novel deep learning architecture that predicts eye
contact while implicitly estimating the head pose. We present a fully automated
system for eye contact detection that solves the sub-problems of end-to-end
feature learning and pose estimation using deep neural networks. To train our
models, we use a dataset comprising 22 hours of 156 play session videos from
over 100 children, half of whom are diagnosed with Autism Spectrum Disorder. We
report an overall precision of 0.76, recall of 0.80, and an area under the
precision-recall curve of 0.79, all of which are significant improvements over
existing methods.
