# A Comprehensive Survey of Masked Faces: Recognition, Detection, and Unmasking
## 2 Related Surveys  
In this section, we look into previous surveys conducted in the fields of MFR and FMR, which serve as essential repositories of recent research and future directions. Despite the relatively short period following the onset of the COVID-19 pandemic, a considerable body of work and surveys has emerged. Figure 2 presents an analysis of related studiesper year for both MFR and FMR from 2019 to 2024, with data sourced from Scopus. For MFR, the search was conducted using keywords such as \"Masked face recognition\" and \"Masked Faces\", while for FMR, keywords such as \"Face mask detection\", \"Face masks\", and \"Mask detection\" were utilized. These surveys offer invaluable insights into the evolution of methodologies, challenges encountered, and advancements made in tackling the intricacies associated with face masks. The objective of this subsection is to provide a succinct overview of select surveys in this domain, thereby situating the current study within the broader context of the existing literature. Face recognition under occlusion predates the COVID-19 era, indicating that masked face recognition is not a novel field but rather a specialized and intricate subset of occluded face recognition. The complexity of masks, ranging from size and color to shape, adds layers of intricacy to MFR. Several surveys have explored partial face recognition, such as the work by [PERSON] et al. [11], which investigates strategies addressing three core challenges in face recognition systems: facial occlusion, single sample per subject (SSPS), and nuances in facial expressions. While offering insights into recent strategies to overcome these hurdles, this survey lacks recent updates and focus on deep learning methods, limiting its applicability to MFR challenges. [PERSON] et al. [12] address real-world complexities like facial expression variability and lighting inconsistencies alongside occlusion challenges. Despite being conducted during the COVID-19 pandemic, this survey overlooks masked face recognition and lacks comprehensive coverage of existing methods and empirical results. Similarly, [PERSON] et al. [13] tackle the persistent challenge of identifying faces obscured by various occlusions, including medical masks. While categorizing modern and conventional techniques for recognizing faces under occlusion, this survey lacks empirical results and comparative analyses of existing approaches addressing occlusion challenges. Conversely, recent surveys have undertaken a comprehensive examination of MFR and FMR, with a focus on addressing the challenges encountered by face recognition and detection systems following the COVID-19 pandemic. Notably, [PERSON] et al. [14] conducted an exhaustive survey on masked face recognition research, which has experienced significant growth and innovation in recent years. The study systematically explores a wide range of methodologies, techniques, and advancements in MFR, with a specific  
Figure 2: Illustrates the evolving landscape of MFR and FMR studies from 2019 to 2024. The data were sourced from Scopus using keywords “Masked face recognition” for MFR and “Face mask detection”, “Face masks”, and “Mask detection” for FMR. emphasis on deep learning approaches. Through a meticulous analysis of recent works, the survey aims to provide valuable insights into the progression of MFR systems. Furthermore, it discusses common benchmark datasets and evaluation metrics in MFR research, offering a robust framework for evaluating different approaches and highlighting challenges and promising research directions in the field. Moreover, [PERSON] et al. (2015) address the pressing need for AI techniques to detect masked faces amidst the COVID-19 pandemic. Their comprehensive analysis includes an examination of existing datasets and categorization of detection methods into conventional and neural network-based approaches. By summarizing recent benchmarking results and outlining future research directions, the survey aims to advance understanding and development in masked facial detection.