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Masked-face recognition using deep metric learning and FaceMaskNet-21.

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  • 1K. J. Somaiya College of Engineering, Vidyavihar Mumbai, 400077 India.

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Summary
This summary is machine-generated.

This study introduces FaceMaskNet-21, a deep learning system for effective masked face recognition. It achieves high accuracy in real-time, aiding security and attendance systems during the COVID-19 pandemic.

Keywords:
CNNCOVID-19Deep metric learningFaceMaskNet-21Masked-face recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • The COVID-19 pandemic necessitates widespread mask usage, rendering traditional facial recognition systems ineffective.
  • Masks obscure key facial features, posing challenges for identity verification in security applications.

Purpose of the Study:

  • To develop an efficient and accurate masked face recognition system.
  • To overcome the limitations of conventional systems in identifying individuals wearing face masks.

Main Methods:

  • Utilized deep metric learning and a novel FaceMaskNet-21 deep learning network.
  • Generated 128-dimensional encodings for face recognition from various image and video sources.
  • Achieved real-time processing with execution times under 10 milliseconds.

Main Results:

  • Attained a testing accuracy of 88.92% for masked face recognition.
  • Demonstrated real-time performance suitable for live video streams and CCTV footage.
  • Validated the system's speed and effectiveness in practical scenarios.

Conclusions:

  • The FaceMaskNet-21 system offers a viable solution for masked face recognition.
  • Its real-time capabilities make it applicable for security, access control, and attendance tracking in diverse environments.
  • The system enhances security and efficiency without requiring mask removal.