The COVID-19 pandemic disrupted people's livelihoods and hindered global trade and transportation. During the COVID-19
pandemic, the World Health Organization has mandated that masks be worn to protect against this deadly virus. Protecting one's
face with a mask has become the standard. Many public service providers will encourage clients to wear masks properly in the
foreseeable future. On the other hand, monitoring the individuals while standing alone in one location is exhausting. This paper
offers a solution based on deep learning for identifying masks worn over faces in public places to minimize the coronavirus
community's transmission. The main contribution of the proposed work is the development of a real-time system for determining
whether the person on the webcam is wearing a mask or not. The ensemble method makes it easier to achieve high accuracy
and makes considerable strides toward enhancing detection speed. In addition, the implementation of transfer learning on pre-trained models and stringent testing on an objective dataset led to the developing a highly dependable and inexpensive solution.
The findings provide validity to the application's potential for use in real-world settings, contributing to the reduction of
pandemic transmission. Compared to the existing methodologies, the proposed method delivers improved accuracy, specificity,
precision, recall, and F-measure performance in three-class outputs. These metrics include accuracy, specificity, precision, and
recall. The appropriate balance is kept between the number of necessary parameters and the time needed to conclude the
various models.