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Related Concept Videos

Prosopagnosia01:24

Prosopagnosia

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Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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Updated: Jun 27, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Facial expressions to identify post-stroke: A pilot study.

Guilherme C Oliveira1, Quoc C Ngo2, Leandro A Passos3

  • 1School of Sciences, São Paulo State University, São Paulo, Brazil; School of Engineering, Royal Melbourne Institute of Technology, Victoria, Australia.

Computer Methods and Programs in Biomedicine
|May 1, 2024
PubMed
Summary
This summary is machine-generated.

Computerized analysis of facial expressions can help paramedics detect stroke symptoms early. This method accurately identifies post-stroke (PS) individuals by analyzing subtle mouth muscle movements, potentially improving treatment timeliness.

Keywords:
Facial action unitFacial expressionMachine learning

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Area of Science:

  • Biomedical Engineering
  • Computer Vision
  • Neurology

Background:

  • Early stroke symptom recognition is crucial for timely treatment and improved patient outcomes.
  • Paramedics frequently miss stroke cases due to challenges in initial symptom identification.
  • Facial expressions represent one of the earliest observable indicators of stroke.

Purpose of the Study:

  • To develop and evaluate a computerized method for analyzing facial expressions to differentiate between post-stroke (PS) individuals and healthy controls (HC).
  • To assess the potential of using action units for objective and automated stroke symptom detection.

Main Methods:

  • Utilized RGB videos from the Toronto Neuroface Dataset featuring 14 PS and 11 HC individuals.
  • Employed action units to analyze specific facial movements during orofacial examinations.
  • Computed action units using XGBoost and performed regression analysis for facial expression classification without manual intervention.

Main Results:

  • Achieved 82% accuracy for the 'Kiss and Spread' facial expression analysis.
  • Demonstrated a high sensitivity of 91% in differentiating PS from HC.
  • Identified features related to mouth muscles as most effective for stroke detection.

Conclusions:

  • This pilot study indicates that automated facial expression analysis can detect post-stroke indicators.
  • Further validation is required in real-world settings across diverse ethnicities and with varying smartphone usage.
  • The proposed method shows promise for smartphone-based screening by first responders to facilitate prompt stroke treatment initiation.