  A core challenge faced by the majority of individuals with Autism Spectrum
Disorder (ASD) is an impaired ability to infer other people's emotions based on
their facial expressions. With significant recent advances in machine learning,
one potential approach to leveraging technology to assist such individuals to
better recognize facial expressions and reduce the risk of possible loneliness
and depression due to social isolation is the design of computer vision-driven
facial expression recognition systems. Motivated by this social need as well as
the low latency requirement of such systems, this study explores a novel deep
time windowed convolutional neural network design (TimeConvNets) for the
purpose of real-time video facial expression recognition. More specifically, we
explore an efficient convolutional deep neural network design for
spatiotemporal encoding of time windowed video frame sub-sequences and study
the respective balance between speed and accuracy. Furthermore, to evaluate the
proposed TimeConvNet design, we introduce a more difficult dataset called
BigFaceX, composed of a modified aggregation of the extended Cohn-Kanade (CK+),
BAUM-1, and the eNTERFACE public datasets. Different variants of the proposed
TimeConvNet design with different backbone network architectures were evaluated
using BigFaceX alongside other network designs for capturing spatiotemporal
information, and experimental results demonstrate that TimeConvNets can better
capture the transient nuances of facial expressions and boost classification
accuracy while maintaining a low inference time.
