⚡ Quick Summary
This study presents a novel masked face recognition model utilizing a Generative Adversarial Network (GAN) and a Dual Scale Adaptive Efficient Attention Network (DS-AEAN) to enhance identification accuracy for individuals wearing masks. The model demonstrates significant improvements in biometric verification processes, addressing a critical need in various sectors.
🔍 Key Details
- 📊 Dataset: Standard datasets containing both masked and mask-free face images
- ⚙️ Technology: Generative Adversarial Network (GAN) and Dual Scale Adaptive Efficient Attention Network (DS-AEAN)
- 🔍 Optimization: Enhanced Addax Optimization Algorithm (EAOA)
- 🏆 Application: Biometric verification in public safety, healthcare, education, and more
🔑 Key Takeaways
- 🛡️ Masked face recognition is crucial for security in various professions.
- 🤖 GAN technology is employed to generate mask-free images from masked inputs.
- 🔄 DS-AEAN processes both generated and original images for accurate identification.
- 📈 Enhanced performance is achieved through the EAOA, maximizing model effectiveness.
- 🌐 The model addresses challenges faced by traditional facial recognition systems in masked scenarios.
- 🔒 Potential applications span across multiple sectors, enhancing security measures.
- 📅 Published in 2025 in the journal Sci Rep, showcasing cutting-edge research.
📚 Background
The rise of face masks in everyday life, particularly due to health crises, has necessitated advancements in facial recognition technology. Traditional systems struggle to accurately identify individuals wearing masks, leading to a demand for innovative solutions that can maintain security without compromising privacy. This study aims to bridge that gap by developing a robust model for masked face recognition.
🗒️ Study
The research involved creating a comprehensive framework for masked face identification, utilizing both masked and unmasked images from established datasets. The GAN model was trained to generate realistic images that could be used to enhance the recognition process, while the DS-AEAN was employed to ensure accurate identification of individuals in various scenarios.
📈 Results
The proposed model demonstrated a marked improvement in recognition accuracy compared to existing systems. By effectively generating mask-free images and utilizing advanced attention mechanisms, the model significantly enhanced the biometric verification process. The results indicate a promising future for the application of this technology in real-world settings.
🌍 Impact and Implications
The implications of this research are profound, particularly in sectors where security and identification are paramount. By improving the accuracy of masked face recognition, this model can enhance safety protocols in public spaces, healthcare facilities, and educational institutions. The integration of such technology could lead to a more secure environment while respecting individual privacy.
🔮 Conclusion
This study highlights the potential of advanced machine learning techniques in addressing contemporary challenges in facial recognition. The development of a GAN-based model for masked face recognition not only showcases technological innovation but also emphasizes the importance of adapting security measures to current societal needs. Continued research in this area is essential for further advancements and applications.
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A generative adversarial network-based accurate masked face recognition model using dual scale adaptive efficient attention network.
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.
Author: [‘Alzubi JA’, ‘Pokkuluri KS’, ‘Arunachalam R’, ‘Shukla SK’, ‘Venugopal S’, ‘Arunachalam K’]
Journal: Sci Rep
Citation: Alzubi JA, et al. A generative adversarial network-based accurate masked face recognition model using dual scale adaptive efficient attention network. A generative adversarial network-based accurate masked face recognition model using dual scale adaptive efficient attention network. 2025; 15:17594. doi: 10.1038/s41598-025-02144-2