  Autism is a developmental disorder that affects social interaction and
communication of children. The gold standard diagnostic tools are very
difficult to use and time consuming. However, diagnostic could be deduced from
child gaze preferences by looking a video with social and abstract scenes. In
this work, we propose an algorithm based on convolutional neural networks to
predict gaze direction for a fast and effective autism diagnosis. Early results
show that our algorithm achieves real-time response and robust high accuracy
for prediction of gaze direction.
