GAN-Based Facial Feature Reconstruction for Improved Masked Face Recognition During Covid

The use of face masks during is a major obstacle has been faced during the COVID-19 epidemic. Face recognition technologies, which rely on unobstructed facial features for accurate identification. In this paper, we make use of Generative Adversarial Networks (GANs) for face reconstruction from masked faces. We propose a GAN-based approach to learn the underlying relationships between masked and unmasked facial features and generate plausible reconstructions of the missing facial features. Our primary goal is to evaluate the effectiveness of GAN-based face reconstruction for masked faces using various metrics, including visual quality, accuracy, and robustness. The findings of this investigation show that the suggested strategy can produce high-quality and precise reconstructions of the missing facial features, and it can be used for face recognition tasks. Our findings suggest that GAN-based face reconstruction has the potential to overcome the limitations posed by face masks, providing a solution that preserves individual privacy and security while ensuring accurate face recognition. This research project has significant implications for the development of new face recognition technologies that can operate effectively in the presence of masks, contributing to the ongoing efforts to combat the COVID-19 pandemic.