Reconstructing Masked Face Using GAN Technique

The COVID-19 pandemic has made face recognition and identification a complex task, as people often cover a significant portion of their face with masks as a precautionary measure. This creates difficulties for biometric devices and secure authentication systems, as masks obstruct facial key points that are necessary for face detection. The presence of masks also presents challenges for face identification. There is a shortage of paired and aligned face images that show faces both with and without masks. This study proposes a framework for reconstructing the occluded part of the face that is covered by a mask. The GAN-based unpaired image translation method is used to translate masked face images into unmasked face images as the reconstructed faces. A synthetic paired face dataset is created to evaluate the performance of the model in reconstructing the unmasked face from a masked face and is used to train the proposed GAN-based face reconstruction model. The model is based on transfer learning and the pix2pix cGAN architecture and the results of the comparison analysis show that our model outperforms other state-of-the-art face reconstruction models in both quality and quantity.