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<p>Masked identification of faces is necessary for authentication purposes. Face masks are frequently utilized in a wide range of professions and sectors including public safety, health care, schooling, catering services, production, sales, and shipping. In order to solve this issue and provide precise identification and verification in masked events, masked facial recognition equipment has emerged as a key innovation. Although facial recognition is a popular and affordable biometric security solution, it has several difficulties in correctly detecting people who are wearing masks. As a result, a reliable method for identifying the masked faces is required. In this developed model, a deep learning-assisted masked face identification framework is developed to accurately recognize the person’s identity for security concerns. At first, the input images are aggregated from standard datasets. From the database, both the masked face images and mask-free images are used for training the Generative Adversarial Network (GAN) model. Then, the collected input images are given to the GAN technique. If the input is a masked face image, then the GAN model generates a mask-free face image and it is considered as feature set 1. If the input is a mask-free image, then the GAN model generates a masked face image and these images are considered as feature set 2. If the input images contain both masked and mask-free images, then it is directly given to Dual Scale Adaptive Efficient Attention Network (DS-AEAN). Otherwise, generated feature set 1 and feature set 2 are given to the DS-AEAN for recognizing the faces to ensure the person’s identity. The effectiveness of this model is further maximized using the Enhanced Addax Optimization Algorithm (EAOA). This model is helpful for a precise biometric verification process. The outcomes of the designed masked face recognition model are evaluated with the existing models to check its capability.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月21日

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  • Jafar A. Alzubi,
  • Kiran Sree Pokkuluri,
  • Rajesh Arunachalam,
  • Surendra Kumar Shukla,
  • Sumanth Venugopal,
  • Karthikayen Arunachalam

摘要

Masked identification of faces is necessary for authentication purposes. Face masks are frequently utilized in a wide range of professions and sectors including public safety, health care, schooling, catering services, production, sales, and shipping. In order to solve this issue and provide precise identification and verification in masked events, masked facial recognition equipment has emerged as a key innovation. Although facial recognition is a popular and affordable biometric security solution, it has several difficulties in correctly detecting people who are wearing masks. As a result, a reliable method for identifying the masked faces is required. In this developed model, a deep learning-assisted masked face identification framework is developed to accurately recognize the person’s identity for security concerns. At first, the input images are aggregated from standard datasets. From the database, both the masked face images and mask-free images are used for training the Generative Adversarial Network (GAN) model. Then, the collected input images are given to the GAN technique. If the input is a masked face image, then the GAN model generates a mask-free face image and it is considered as feature set 1. If the input is a mask-free image, then the GAN model generates a masked face image and these images are considered as feature set 2. If the input images contain both masked and mask-free images, then it is directly given to Dual Scale Adaptive Efficient Attention Network (DS-AEAN). Otherwise, generated feature set 1 and feature set 2 are given to the DS-AEAN for recognizing the faces to ensure the person’s identity. The effectiveness of this model is further maximized using the Enhanced Addax Optimization Algorithm (EAOA). This model is helpful for a precise biometric verification process. The outcomes of the designed masked face recognition model are evaluated with the existing models to check its capability.