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A generative adversarial network-based accurate masked face recognition model using dual scale adaptive efficient

Jafar A Alzubi1, Kiran Sree Pokkuluri2, Rajesh Arunachalam3

  • 1Faculty of Engineering, Al-Balqa Applied University, Salt, 19117, Jordan.

Scientific Reports
|May 21, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning framework for accurate masked face recognition, crucial for security and authentication. The model effectively identifies individuals even when their faces are partially obscured by masks.

Keywords:
Dual scale adaptive efficient attention networkEnhanced addax optimization algorithmGenerative adversarial networkMasked face recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biometrics

Background:

  • Face masks are common in various professions, posing challenges for traditional facial recognition systems.
  • Accurate identification of masked individuals is essential for security and authentication purposes.
  • Existing facial recognition methods struggle with detecting masked faces, necessitating advanced solutions.

Purpose of the Study:

  • To develop a deep learning-assisted framework for accurate masked face identification.
  • To enhance biometric verification processes in scenarios involving face masks.
  • To address the limitations of current facial recognition technology in recognizing masked individuals.

Main Methods:

  • Utilized a Generative Adversarial Network (GAN) to generate mask-free versions of masked faces and masked versions of unmasked faces.
  • Employed a Dual Scale Adaptive Efficient Attention Network (DS-AEAN) for feature extraction and face recognition.
  • Optimized the model's performance using the Enhanced Addax Optimization Algorithm (EAOA).

Main Results:

  • The developed framework demonstrated effective recognition of masked faces.
  • The integration of GAN and DS-AEAN improved the accuracy of biometric verification.
  • Performance evaluation showed the model's capability compared to existing methods.

Conclusions:

  • The proposed deep learning model offers a reliable solution for masked face recognition.
  • This framework enhances security by enabling precise identity verification with masked faces.
  • The study contributes a robust method for biometric authentication in diverse real-world applications.