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

TL;DR

This paper presents a deep learning system combining CNN and RNN to accurately identify facial emotions in children with ASD, aiding early diagnosis and intervention.

Contribution

It introduces the RCNN-FER system that leverages facial landmarks and recurrent neural networks for improved emotion recognition in autistic children.

Findings

Higher accuracy in emotion detection compared to traditional models

Reduced time complexity in emotion prediction

Effective early biomarker identification for ASD

Abstract

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.