GAN-based Face Reconstruction for Masked-Face

Facial recognition and identification which play an important role in human-computer interaction, secure authentication and criminal face recognition, are impeded by the advent of face masks due to COVID-19 pandemic. This is a challenging problem due to the following reasons: (i) masks cover quite a large part of the face even below the chin, (ii) it is not possible to collect and prepare a real paired-face images with and without mask object, (iii) face alterations and the presence of different masks is even more challenging. In this work, we propose a general framework that can be used to reconstruct the hidden part of face concealed by mask. We have employed GAN-based unpaired domain translation technique to translate masked face images from the source to the unmasked images in the destination domain. To this end, we also create a paired datasets of real face images and synthesized correspondence’s with face-masks and use it towards training of our proposed GAN-based facial reconstruction system which can be used for facial identification and secure authentication in human-computer interaction. The obtained results demonstrate that our model outperforms other representative state-of-the-art face completion approaches both qualitatively and quantitatively.