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Muscles for Facial Expressions01:14

Muscles for Facial Expressions

The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
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Related Experiment Video

Updated: Jul 18, 2026

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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RETRACTED ARTICLE: Ambiguous facial expression detection for Autism Screening using enhanced YOLOv7-tiny model

Akhil Kumar1, Ambrish Kumar1, Dushantha Nalin K Jayakody2,3

  • 1School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India.

Scientific Reports
|November 18, 2024
PubMed
Summary

This study introduces a novel method for detecting autism spectrum disorder (ASD) in children using facial attributes. An improved YOLOv7-tiny model accurately identifies autism-related facial features, aiding early diagnosis.

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Area of Science:

  • Computer Vision
  • Developmental Psychology
  • Machine Learning

Background:

  • Autism spectrum disorder (ASD) is a developmental condition impacting social and behavioral skills in children.
  • Early detection of ASD is crucial for improving cognitive abilities and quality of life.
  • Current detection methods rely on cognitive tests and physical activities.

Purpose of the Study:

  • To detect autism spectrum disorder (ASD) in children using facial attributes from images.
  • To develop an enhanced deep learning model for identifying subtle facial differences in children with ASD.
  • To improve early diagnostic capabilities for ASD through computer vision techniques.

Main Methods:

  • An improvised variant of the YOLOv7-tiny model was developed for facial attribute detection.
  • The model integrates dilated convolutional layers and an additional YOLO detection head to enhance feature extraction and recognition.
  • The model was trained and evaluated on a self-annotated dataset of children's faces.

Main Results:

  • The developed model achieved a mean Average Precision (mAP) of 79.56%.
  • Performance surpassed the baseline YOLOv7-tiny and the state-of-the-art YOLOv8 Small models.
  • The model successfully detected faces with autism-related features, providing bounding boxes and confidence scores.

Conclusions:

  • Facial attributes can be effectively utilized for the detection of autism spectrum disorder (ASD).
  • The proposed enhanced YOLOv7-tiny model demonstrates superior performance in identifying ASD-related facial features.
  • This research offers a promising non-invasive approach for early ASD screening.