Automatic approach for mask detection: effective for COVID-19

What is it about?

The outbreak of coronavirus disease 2019 (COVID-19) occurred at the end of 2019, and it has continued to be a source of misery for millions of people and companies well into 2020. There is a surge of concern among all persons, especially those who wish to resume in-person activities, as the globe recovers from the epidemic and intends to return to a level of normalcy. Wearing a face mask greatly decreases the likelihood of viral transmission and gives a sense of security, according to studies. However, manually tracking the execution of this regulation is not possible. The key to this is technology. We present a deep learning-based system that can detect instances of improper use of face masks. A dual-stage convolutional neural network architecture is used in our system to recognize masked and unmasked faces. This will aid in the tracking of safety breaches, the promotion of face mask use, and the maintenance of a safe working environment. In this paper, we propose a variant of a multi-face detection model which has the potential to target and identify a group of people whether they are wearing masks or not.

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Why is it important?

The proposed deep learning-based system utilizing a dual-stage convolutional neural network architecture is critically important as it offers a technological solution to promote adherence to face mask regulations, essential for preventing the spread of COVID-19. This innovation aids in maintaining public health safety by automatically detecting improper mask usage, thereby supporting the transition towards normalcy while ensuring a safe environment for in-person activities.

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