Web-Based Facial Emotion Recognition System for Enhancing Social Interaction in Autistic Spectrum Disorder Children Using CNN & Flask Framework

The children with ASD have difficulty in showing empathy and also in recognizing and identifying emotions. This research introduces a web-based facial emotion recognition system designed to assist ASD children in recognizing and understanding emotions in others, ultimately enhancing their social skills. The system is implemented using the Flask web framework & CNN, providing a user-friendly interface for both children and caregivers or therapists. This study employs deep learning to detect face and analyze facial expressions and accurately classify emotion out of 6 emotions i.e., fear, angry, joy, natural, sadness, surprise displayed by individuals with ASD A CNN-based FER model trained on an autistic children dataset helps children with ASD recognize and understand facial emotions, a skill crucial for successful social interactions, targeting 6-12 year old. The CNN model has potential applications of FER in real-time social scenarios comparing with the existing methods like facial coding system, local binary pattern etc. our model aims to provide personalized and interactive learning environment where the facial emotions of children with ASD are detected through web cam during a learning session and unique emotion is detected which is graphically represented at the end of the session with the benefit of web based flask framework to assist educators, therapists to improve learning techniques, social skills among ASD children. Ultimately promoting a better quality of life for people who belong in the autistic spectrum.