Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Abstract

After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas of smart cities requires modern technologies, such as deep learning and deep transfer learning, and computer vision for automatic face mask detection and accurate control of whether people wear masks correctly. This paper reviews the progress in face mask detection research, emphasizing deep learning and deep transfer learning techniques. Existing face mask detection datasets are first described and discussed before presenting recent advances to all the related processing stages using a well-defined taxonomy, the nature of object detectors and Convolutional Neural Network architectures employed and their complexity, and the different deep learning techniques that have been applied so far. Moving on, benchmarking results are summarized, and discussions regarding the limitations of datasets and methodologies are provided. Last but not least, future research directions are discussed in detail.

An automated face mask detection system using transfer learning based neural network to preventing viral infection

“…Fivelayer architecture is added to the pre-trained MobileNetV2 architecture in this study to improve classification accuracy for face mask detection using fewer training parameters. Here (Himeur et al, 2023), it was shown that the FMD is a complex problem that presents several difficulties for computer vision and machine learning engineers. Detecting a face that has been covered or a mask that has been properly worn are issues related to the right specification of the task, and they impact the model that must be trained to do the relevant work properly.…”

“…Five‐layer architecture is added to the pre‐trained MobileNetV2 architecture in this study to improve classification accuracy for face mask detection using fewer training parameters. Here (Himeur et al, 2023), it was shown that the FMD is a complex problem that presents several difficulties for computer vision and machine learning engineers.…”

An effective stacked autoencoder based depth separable convolutional neural network model for face mask detection

“…In recent studies [ 25 , 26 ], the authors used directional gradients techniques to recognise the face and expressions of face. Yassine et al [ 27 ] published a review paper on the recent works in face mask detection. The paper described the various parameters that have been used to evaluate the face mask detection.…”

Face mask classification using convolutional neural networks with facial image regions and super resolution

“…Especially, during world public health emergency situation like COVID-19 pandemic, face mask detection system is one of many safety measures until today as a new normal way of life. Therefore, to automatically detect a person's face with or without mask using computer vision approaches, many previous techniques were developed [4]. Mostly, those techniques are supervised masked face classification from machine learning models applied to detect crowd or individuals wearing the mask.…”