Masked Face Restoration Using GANs: A Survey on Recognition, Detection, and Inpainting

Abstract

Emergence of masked face recognition (MFR) as a pivotal area in biometric identification has been significantly accelerated by the global COVID-19 pandemic. In response, the research community has developed a variety of innovative techniques to address recognition and detection under occlusion, with a growing emphasis on Generative Adversarial Networks (GANs) for masked face restoration and inpainting. We examined three interconnected sub-domains: Masked Face Recognition (MFR), Face Mask Detection, and Face Unmasking (FU), each addressing unique aspects of the problem from identifying individuals with partially or fully covered faces to reconstructing occluded facial regions for improved accuracy. The core focus of this paper is on the role of GANs in overcoming occlusion by synthesizing realistic facial textures in the masked regions, thereby restoring the identity cues. Beyond technical developments, the paper analyzes the limitations and open research problems, such as maintaining identity consistency in restored images, handling diverse mask types and occlusion levels, and ensuring generalizability across different demographic groups and environments. By integrating insights from recent advances and identifying existing research gaps, this survey aims to serve as a comprehensive reference for academics and practitioners engaged in the development of robust, privacy-aware, and ethically responsible masked face recognition systems enhanced by GANs.