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Vision Transformer (VIT) Architecture for Robust Masked Face Recognition

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  • Lekha Prajapati

    (Research Scholar, Department of Computer Science, Taywade College Koradi, (M.S.), India.)

  • Girish Katkar

    (Assistant Professor, Department of Computer Science, Taywade College Koradi, (M.S.), India.)

  • Ajay Ramteke

    (Assistant Professor, Department of Computer Science, Taywade College Koradi, (M.S.), India.)

Abstract

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.

Suggested Citation

  • Lekha Prajapati & Girish Katkar & Ajay Ramteke, 2026. "Vision Transformer (VIT) Architecture for Robust Masked Face Recognition," International Journal of Latest Technology in Engineering, Management & Applied Science, RSIS International, vol. 15(3), pages 140-146, March.
    • Handle: RePEc:bjf:ijltem:v:15:y:2026:i:3:a:2176
    DOI: 10.51583/IJLTEMAS.2026.150300014
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