Brief Review of Deep Learning Techniques employed in Face Mask Classification

This review article presents recent advancements in utilizing deep learning techniques (DLT) and algorithms for face mask classification (FMC). It addresses challenges associated with FMC, such as variations in mask designs, lighting conditions, and occlusion. The paper provides a concise overview of deep learning algorithms (DLA), including Mask R-CNNs, Region-based CNNs (R-CNNs), and Convolutional Neural Networks (CNNs). Evaluation of these algorithms is conducted, emphasizing their performance through metrics like accuracy, precision, and recall. Finally, the article concludes by outlining current research gaps and suggesting future directions in face mask classification using DLT.