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Related Concept Videos

Prosopagnosia01:24

Prosopagnosia

159
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...
159

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Smart Recognition COVID-19 System to Predict Suspicious Persons Based on Face Features.

Mossaad Ben Ayed1,2, Ayman Massaoudi3,4, Shaya A Alshaya1

  • 1Computer Science Department, College of Sciences and Humanities Sciences At alGhat, Majmaah University, Majmaah, 11952 Saudi Arabia.

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This study introduces a Smart Recognition COVID-19 (SRC) system to detect shortness of breath, a key symptom of coronavirus disease (COVID-19), using video-based heart rate estimation for airport screening.

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Breath score estimationCOVID-19Hear rate estimationSARS-CoV-2Suspicious passengerVideo processing

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Respiratory Medicine

Background:

  • The global spread of coronavirus disease (COVID-19) necessitates rapid and effective screening methods.
  • Airport environments require non-invasive tools to identify individuals with respiratory distress, a primary COVID-19 symptom.
  • Current screening methods may not adequately capture subtle respiratory anomalies.

Purpose of the Study:

  • To develop a Computer-Aided Diagnosis (CAD) framework for predicting COVID-19 suspicion.
  • To identify individuals exhibiting shortness of breath, a critical indicator of COVID-19.
  • To propose a novel method for extracting respiratory anomalies from facial video analysis.

Main Methods:

  • Development of a Smart Recognition COVID-19 (SRC) system.
  • Utilizing face-based video analysis to estimate heart rate.
  • Deriving a breath score to quantify respiratory status.

Main Results:

  • The SRC system accurately estimates breath rate with an average error of approximately 1 breath per minute.
  • The system successfully extracts shortness of breath anomalies from video data.
  • Achieved a high degree of accuracy in breath score estimation.

Conclusions:

  • The proposed SRC system offers a valuable tool for airport authorities to predict potentially infected passengers.
  • This non-invasive approach aids in early identification of individuals with respiratory compromise.
  • The technology supports public health measures by facilitating targeted screening in high-traffic areas.