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Masking and Demasking Agents

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Deep learning techniques for detecting and recognizing face masks: A survey.

Rahaf Alturki¹, Maali Alharbi¹, Ftoon AlAnzi¹

¹Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia.

Frontiers in Public Health

|October 13, 2022

Summary

This summary is machine-generated.

Artificial intelligence and deep learning models were surveyed for Masked Face Recognition (MFR) and Occluded Face Recognition (OFR) to ensure correct mask usage. These AI techniques effectively monitored if individuals wore face masks properly, covering both nose and mouth.

Keywords:

convolutional neural network crowd monitoring face mask public health transfer learning

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Area of Science:

Computer Science
Artificial Intelligence
Biomedical Engineering

Background:

The COVID-19 pandemic necessitated widespread adoption of face masks to curb disease transmission.
Human error in mask usage, such as improper covering of the nose and mouth, significantly reduced mask efficacy.
Monitoring correct face mask adherence became crucial for public health initiatives.

Purpose of the Study:

To survey and evaluate deep learning techniques for Masked Face Recognition (MFR) and Occluded Face Recognition (OFR).
To assess the capability of artificial intelligence in detecting correct face mask usage.
To address the challenge of incorrect face mask application, including nose and mouth coverage.

Main Methods:

Conducted a survey of existing deep learning algorithms for MFR and OFR.
Utilized AI to analyze facial features and verify proper mask coverage.
Tested algorithms on datasets designed to identify correct and incorrect mask-wearing scenarios.

Main Results:

Deep learning models demonstrated high effectiveness in detecting whether a face mask was worn.
AI algorithms successfully identified incorrect mask usage, such as uncovered noses.
The developed techniques showed promising results in monitoring adherence to face mask guidelines.

Conclusions:

Artificial intelligence offers a viable solution for monitoring correct face mask usage.
Deep learning techniques in MFR and OFR can significantly improve public health compliance.
Further development in AI can enhance the accuracy and reliability of mask-wearing detection systems.