FaceNet-cGAN: A Deep Learning-based Approach for Masked Face Recognition

Masked faces can pose a challenge for security systems, making it easier for individuals to disguise themselves and evade identification. To address this issue, developing a system that can recognize masked faces can help in identifying potential security threats and preventing criminal activities. Therefore, this research proposes a deep learning-based approach, namely FaceNet-based conditional generative adversarial network (FaceNet-cGAN), to detect and remove masks, which would generate a high-quality, unmasked facial image and improve the accuracy of facial recognition systems. The proposed FaceNet-cGAN approach demonstrated superior performance compared to other existing methods, as evidenced by the obtained SSIM score of 92.2% and PSNR value of 29.97 dB.