Real-Time Covid-19 Face Masks Detection Using Convolutional Neural Networks and Artificial Intelligence

We show that the COVID-19 pandemic spread rapidly, having an immediate effect on global commerce and migration. Nowadays, everyone seems to be wearing a face mask as a form of self-defense. Customers of many upcoming public services may soon be required to conceal their identities behind masks. As a result, it is imperative that the civilised world find masks. Using Tensor Flow, Keras, OpenCV, and ScikitLearn, this paper demonstrates a straightforward method for achieving this objective. This method can be used to identify a person's face in a photograph or video and determine if they are hiding their identity behind a mask. Being able to identify a person in video footage in real time, even if they are wearing a disguise, is a powerful surveillance tool. The method achieves a high degree of precision. Careful analysis of the parameters of the Convolutional Neural Network is required to prevent over-fitting in mask detection. (CNN) Cameras and open-source platforms like webcams and surveillance systems are being used in many current studies in the rapidly developing field of cutting-edge technology to detect and diagnose issues in government core areas like company outlets and airlines in real time. Our goal is to use OpenCV and Convolutional Neural Networks to identify COVID-19 face masks and remove personal identifiers (CNNs). Using pre-processing procedures for data argumentation, we were able to enhance the quality of our dataset and the effectiveness of the recommended models