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 th...

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Published in:	Neural computing & applications Vol. 35; no. 17; pp. 12717 - 12728
Main Author:	Talaat, Fatma M.
Format:	Journal Article
Language:	English
Published:	London Springer London 01.06.2023 Springer Nature B.V
Subjects:	Abnormalities Artificial Intelligence Artificial neural networks Autism Autistic children Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Diagnosis Emotion recognition Emotions Feature extraction Image Processing and Computer Vision Machine learning Original Article Probability and Statistics in Computer Science Real time Deep learning Autism Emotion recognition Assistive technology
ISSN:	0941-0643, 1433-3058
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Abstract	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.
AbstractList	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.
Author	Talaat, Fatma M.
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SubjectTerms	Abnormalities Artificial Intelligence Artificial neural networks Autism Autistic children Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Deep learning Diagnosis Emotion recognition Emotions Feature extraction Image Processing and Computer Vision Machine learning Original Article Probability and Statistics in Computer Science Real time
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