Authors :
B.Sathyabama; Karthikeyan R; Raguram S; Jonathan Prince A
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/4jmvptjr
Scribd :
https://tinyurl.com/mvyac7pu
DOI :
https://doi.org/10.5281/zenodo.10142920
Abstract :
An extensive range of social and
communication challenges, frequently accompanied
by repetitive behaviors, describe the complicated
neurodevelopmental disease known as autism
spectrum disorder (ASD). Improving the quality of
life for people with ASD requires early detection and
intervention. The use of computer vision and machine
learning techniques to aid in the early diagnosis and
evaluation of autism has attracted increasing interest
in recent years. Convolutional neural networks
(CNNs) and facial landmarks are used by the FERS to
extract pertinent face features after first recording
facial expressions using picture or video input. The
algorithm then uses a machine learning classifier to
forecast autism severity using the extracted emotional
cues. Accuracy, precision, recall, and F1-score are a
few of the measures used to gauge the classifier's
performance in terms of identifying people with ASD.
The suggested FERS has the potential to provide a
number of advantages, including early autism
detection, objective evaluation of social and emotional
behaviors, and aiding medical practitioners in making
educated judgments about diagnosis and intervention
tactics. Additionally, it might offer a useful tool for
tracking the development of people with ASD through
time.
Keywords :
Autism Spectrum Disorder,Convolutional Neural Networks.
An extensive range of social and
communication challenges, frequently accompanied
by repetitive behaviors, describe the complicated
neurodevelopmental disease known as autism
spectrum disorder (ASD). Improving the quality of
life for people with ASD requires early detection and
intervention. The use of computer vision and machine
learning techniques to aid in the early diagnosis and
evaluation of autism has attracted increasing interest
in recent years. Convolutional neural networks
(CNNs) and facial landmarks are used by the FERS to
extract pertinent face features after first recording
facial expressions using picture or video input. The
algorithm then uses a machine learning classifier to
forecast autism severity using the extracted emotional
cues. Accuracy, precision, recall, and F1-score are a
few of the measures used to gauge the classifier's
performance in terms of identifying people with ASD.
The suggested FERS has the potential to provide a
number of advantages, including early autism
detection, objective evaluation of social and emotional
behaviors, and aiding medical practitioners in making
educated judgments about diagnosis and intervention
tactics. Additionally, it might offer a useful tool for
tracking the development of people with ASD through
time.
Keywords :
Autism Spectrum Disorder,Convolutional Neural Networks.
