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Related Experiment Video

Updated: Jun 7, 2025

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
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RETRACTED ARTICLE: Ambiguous facial expression detection for Autism Screening using enhanced YOLOv7-tiny model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.