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

Jafar A Alzubi; Kiran Sree Pokkuluri; Rajesh Arunachalam; Surendra Kumar Shukla; Sumanth Venugopal; Karthikayen Arunachalam

doi:10.1038/s41598-025-02144-2

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

Sci Rep. 2025 May 21;15(1):17594. doi: 10.1038/s41598-025-02144-2.

Authors

Jafar A Alzubi¹, Kiran Sree Pokkuluri², Rajesh Arunachalam³, Surendra Kumar Shukla⁴, Sumanth Venugopal⁵, Karthikayen Arunachalam⁶

Affiliations

¹ Faculty of Engineering, Al-Balqa Applied University, Salt, 19117, Jordan.
² Department of Computer Science and Engineering, Shri Vishnu Engineering College for Women, Bhimavaram, Andhra Pradesh, 534202, India.
³ Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, 602105, India.
⁴ Department of Computer Science and Engineering , Amity School of Engineering and Technology, Amity University, Noida, Uttar Pradesh, India.
⁵ Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India. sumanth.v@manipal.edu.
⁶ Department of Electronics and Communication Engineering, P.T. Lee Chengalvaraya Naicker College of Engineering and Technology, Oovery, Kanchipuram, Tamil Nadu, India.

Abstract

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.

Keywords: Dual scale adaptive efficient attention network; Enhanced addax optimization algorithm; Generative adversarial network; Masked face recognition.

MeSH terms

Automated Facial Recognition* / methods
Biometric Identification* / methods
Deep Learning
Face* / anatomy & histology
Facial Recognition*
Generative Adversarial Networks*
Humans
Image Processing, Computer-Assisted / methods
Masks
Neural Networks, Computer