Vol.35, No.1, 2023, pp.595-607, doi:10.32604/iasc.2023.027562
Emotion Exploration in Autistic Children as an Early Biomarker through R-CNN
  • S. P. Abirami1,*, G. Kousalya1, R. Karthick2
1 Coimbatore Institute of Technology, Coimbatore, India
2 Steps Rehabilitation Center (I), Steps Groups, Coimbatore, India
* Corresponding Author: S. P. Abirami. Email:
Received 20 January 2022; Accepted 01 March 2022; Issue published 06 June 2022
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 Convolution Neural Network (CNN) using 68 landmark points plotted on the frontal face with a prediction network formed by Recurring Neural Network (RNN) known as the RCNN based Facial Expression Recommendation (FER) system. The paper adopts Recurring Convolution Neural Network (R-CNN) to take the advantage of increased accuracy and performance with decreased time complexity in predicting emotion as textual network analysis. The papers prove better accuracy in identifying the emotion in autistic children when compared over simple machine learning models built for such identifications contributing to autistic society.
Autism spectrum disorder; facial expression; emotion; CNN; RNN
Cite This Article
S. P. Abirami, G. Kousalya and R. Karthick, "Emotion exploration in autistic children as an early biomarker through r-cnn," Intelligent Automation & Soft Computing, vol. 35, no.1, pp. 595–607, 2023.
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