Novel Face Mask Detection Technique using Machine Learning to control COVID'19 pandemic

Area of Science:

• Computer Science
• Public Health
• Machine Learning

Background:

• The COVID-19 pandemic has caused widespread mortality and societal disruption.
• Inadequate antiviral treatments and common transmission routes (breathing, coughing, sneezing) facilitate rapid virus spread.
• Face masks are crucial personal protective equipment (PPE) for mitigating transmission, especially in public spaces.

Purpose of the Study:

• To develop an automated system for detecting face mask usage.
• To address the impracticality and cost of manual inspection for mask compliance.
• To integrate a mask detection system with CCTV for enhanced public safety.

Main Methods:

• Utilized a machine learning facial categorization system.
• Developed a mask detector algorithm.
• Designed for integration with existing Closed-Circuit Television (CCTV) infrastructure.

Main Results:

• Successfully created a functional mask detection system.
• The system accurately identifies individuals wearing or not wearing face masks.
• The technology is suitable for real-time monitoring in public areas.

Conclusions:

• Automated mask detection using machine learning offers a scalable solution for enforcing mask mandates.
• This technology can improve compliance with public health guidelines.
• Integration with CCTV systems provides a practical method for ensuring mask usage in high-traffic environments.