Real-time facial emotion recognition system among children with autism based on deep learning and IoT

Diagnosis of autism considers a challenging task for medical experts since the medical diagnosis mainly depends on the abnormalities in the brain functions that may not appear in the early stages of early onset of autism disorder. Facial expression can be an alternative and efficient solution for the early diagnosis of Autism. This is due to Autistic children usually having distinctive patterns which facilitate distinguishing them from normal children. Assistive technology has proven to be one of the most important innovations in helping people with autism improve their quality of life. A real-time emotion identification system for autistic youngsters was developed in this study. Face identification, facial feature extraction, and feature categorization are the three stages of emotion recognition. A total of six facial emotions are detected by the propound system: anger, fear, joy, natural, sadness, and surprise. This section proposes an enhanced deep learning (EDL) technique to classify the emotions using convolutional neural network. The proposed emotion detection framework takes the benefit from using fog and IoT to reduce the latency for real-time detection with fast response and to be a location awareness. From the results, EDL outperforms other techniques as it achieved 99.99% accuracy. EDL used GA to select the optimal hyperparameters for the CNN.