(untitled) (International Journal of Latest Technology in Engineering Management & Applied Science, 2026)
The widespread adoption of facial masks during the COVID-19 pandemic significantly challenged existing facial recognition systems by occluding critical biometric features. This paper proposes a Vision Transformer (ViT) based approach for robust Masked Face Recognition (MFR). Unlike traditional Convolutional Neural Networks (CNNs) that rely on local receptive fields, the ViT architecture utilizes global self-attention to capture long-range dependencies, making it more resilient to the information loss caused by masks. We evaluate our approach on the MFR2 dataset, by implementing a standardized training methodology, and our model achieves a peak accuracy of 98.22%. This study demonstrates that transformer-based architectures, combined with specialized attention mechanisms and contrastive learning, offer a state-of-the-art solution for secure authentication in masked environments.