  Autism Spectrum Disorder (ASD) is found to be a major concern among various
occupational therapists. The foremost challenge of this neurodevelopmental
disorder lies in the fact of analyzing and exploring various symptoms of the
children at their early stage of development. Such early identification could
prop up the therapists and clinicians to provide proper assistive support to
make the children lead an independent life. Facial expressions and emotions
perceived by the children could contribute to such early intervention of
autism. In this regard, the paper implements in identifying basic facial
expression and exploring their emotions upon a time variant factor. The
emotions are analyzed by incorporating the facial expression identified through
CNN using 68 landmark points plotted on the frontal face with a prediction
network formed by RNN known as RCNN-FER system. The paper adopts R-CNN to take
the advantage of increased accuracy and performance with decreased time
complexity in predicting emotion as a textual network analysis. The papers
proves better accuracy in identifying the emotion in autistic children when
compared over simple machine learning models built for such identifications
contributing to autistic society.
