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Abstract

The coronavirus disease 2019 outbreak caused widespread disruption. The World Health Organization has recommended wearing face masks, along with other public health measures, such as social distancing, following medical guidelines, and thermal scanning, to reduce transmission, reduce the burden on healthcare systems, and protect population groups. However, wearing a mask, which acts as a barrier or shield to reduce transmission of infection from infected individuals, hides most facial features, such as the nose, mouth, and chin, on which face detection systems depend, which leads to the weakness of these systems. This paper aims to provide essential insights for researchers and practitioners interested in developing and implementing deep learning-based face mask detection systems. Although current deep learning models have made significant strides and tremendous advances in many applications, including security, access control, and identity verification, development efforts continue. This paper also discusses the importance of the datasets used to train and evaluate these models, emphasising the need for diverse data such as mask types, facial occlusions, lighting conditions, and high-quality data to enhance model performance. In addition to the challenges posed by real-world conditions that can affect detection accuracy, face detection and face mask recognition methods were compared to deep learning models, where the accuracy of the Multi-Task ArcFace model reached 99.78, which is the highest accuracy among other detection methods.

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