  In this paper, we introduce an end-to-end machine learning-based system for
classifying autism spectrum disorder (ASD) using facial attributes such as
expressions, action units, arousal, and valence. Our system classifies ASD
using representations of different facial attributes from convolutional neural
networks, which are trained on images in the wild. Our experimental results
show that different facial attributes used in our system are statistically
significant and improve sensitivity, specificity, and F1 score of ASD
classification by a large margin. In particular, the addition of different
facial attributes improves the performance of ASD classification by about 7%
which achieves a F1 score of 76%.
