Abstract
This review offers a comprehensive and in-depth analysis of face mask detection and recognition technologies, emphasizing their critical role in both public health and technological advancements. Existing detection methods are systematically categorized into three primary classes: feaRture-extraction-and-classification-based approaches, object-detection-models-based methods and multi-sensor-fusion-based methods. Through a detailed comparison, their respective workflows, strengths, limitations, and applicability across different contexts are examined. The review underscores the paramount importance of accurate face mask detection, especially in response to global public health challenges such as pandemics. A central focus is placed on the role of datasets in driving algorithmic performance, addressing key factors, including dataset diversity, scale, annotation granularity, and modality. The integration of depth and infrared data is explored as a promising avenue for improving robustness in real-world conditions, highlighting the advantages of multimodal datasets in enhancing detection capabilities. Furthermore, the review discusses the synergistic use of real-world and synthetic datasets in overcoming challenges such as dataset bias, scalability, and resource scarcity. Emerging solutions, such as lightweight model optimization, domain adaptation, and privacy-preserving techniques, are also examined as means to improve both algorithmic efficiency and dataset quality. By synthesizing the current state of the field, identifying prevailing challenges, and outlining potential future research directions, this paper aims to contribute to the development of more effective, scalable, and robust face mask detection systems for diverse real-world applications.