Exploring the pattern of Emotion in children with ASD as an early biomarker through Recurring-Convolution Neural Network (R-CNN)
This work addresses early intervention for children with ASD, but it appears incremental as it applies an existing R-CNN method to a specific domain.
The paper tackled early identification of Autism Spectrum Disorder (ASD) by analyzing facial expressions and emotions in children using a Recurring-Convolution Neural Network (R-CNN), achieving better accuracy compared to simple machine learning models.
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.