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Autism Spectrum Disorder (ASD) is found to be a major concern among variousoccupational therapists. The foremost challenge of this neurodevelopmentaldisorder lies in the fact of analyzing and exploring various symptoms of thechildren at their early stage of development. Such early identification couldprop up the therapists and clinicians to provide proper assistive support tomake the children lead an independent life. Facial expressions and emotionsperceived by the children could contribute to such early intervention ofautism. In this regard, the paper implements in identifying basic facialexpression and exploring their emotions upon a time variant factor. Theemotions are analyzed by incorporating the facial expression identified throughCNN using 68 landmark points plotted on the frontal face with a predictionnetwork formed by RNN known as RCNN-FER system. The paper adopts R-CNN to takethe advantage of increased accuracy and performance with decreased timecomplexity in predicting emotion as a textual network analysis. The papersproves better accuracy in identifying the emotion in autistic children whencompared over simple machine learning models built for such identificationscontributing to autistic society.

Exploring the pattern of Emotion in children with ASD as an early biomarker through Recurring-Convolution Neural Network (R-CNN)