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Multiclass Mask Classification with a New Convolutional Neural Model and Its Real-Time Implementation.

Alexis Campos1, Patricia Melin1, Daniela Sánchez1

  • 1Tijuana Institute of Technology, TecNM, Tijuana 22379, Mexico.

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Summary
This summary is machine-generated.

This study introduces a real-time system using a convolutional neural network (CNN) to detect mask usage, classifying individuals as correctly masked, incorrectly masked, or unmasked to reduce virus transmission.

Keywords:
COVID-19computer visionconvolutional neural networkface mask classificationreal-time

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Public Health

Background:

  • The COVID-19 pandemic necessitated new strategies to mitigate virus spread.
  • Reduced social interaction and increased reliance on technology became crucial.
  • Implementing systems to monitor public health measures like mask-wearing is essential.

Purpose of the Study:

  • To develop a real-time system for identifying face regions and classifying mask usage.
  • To create a novel convolutional neural network (CNN) model for accurate mask detection.
  • To aid in monitoring and alarming individuals not adhering to mask policies.

Main Methods:

  • Utilized preprocessing techniques for face region identification.
  • Developed a new convolutional neural network (CNN) model for classification.
  • Implemented the system on a Raspberry Pi 4 for real-time application.

Main Results:

  • Achieved 99.69% accuracy on the MaskedFace-Net dataset.
  • Successfully classified individuals into three categories: correctly masked (green), incorrectly masked (yellow), and unmasked (red).
  • Demonstrated the effectiveness of CNNs in real-time face and mask detection.

Conclusions:

  • CNN models are highly effective for real-time face and mask detection tasks.
  • The developed system can significantly contribute to reducing virus transmission.
  • The system offers a practical solution for monitoring mask compliance in public spaces.