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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.
Insights
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.
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.
Abstract:
The coronavirus (COVID-19) is identified at first in Wuhan in December 2019. The apparition of the COVID-19 virus is widely spread to concern all countries worldwide. The World Health Organization (WHO) on March 11 declare COVID-19 a pandemic. This Virus causes a serious infection of the respiratory system. Its high transmission constitutes great problems and challenges. The WHO proposes many actions to limit the spread of the virus such as quarantine and decrease or halt flights between states. The actions taken by states in airports are to detect suspicious persons with COVID-19. We aimed to provide a Computer-Aided Diagnosis (CAD) framework to predict suspicious COVID-19 person. This prediction identifies suspicious persons who suffer from shortness breath which is the main symptom of this disease. Extract shortness breath anomaly through the estimated heart rate from face based-video is the main contribution of the present paper. We developed a Smart Recognition COVID-19 (SRC) system to estimate the breath score. In conclusion, our study achieves an accurate breath score. The error is about 1 breath per minute. The proposed solution is of great importance because it helps managers in the airport to predict suspicious COVID-19 passengers.
