Every person in the pandemic has to put on a mask to stop the CORONA virus from spreading. In these difficult COVID-19 times, developing a model that detects people with and without masks in real-time is critical as a simple precautionary measure to prevent virus spread. If used correctly, this machine learning technique can help frontline warriors simplify their work while also saving their lives. Tensor Flow, Keras, and OpenCV are used in the development of a convolutional neural network (CNN) model, which helps the algorithm make the most accurate predictions. The Java-script API facilitates webcam access for face mask detection in real time. The first stage, known as preprocessing, consists of "grayscale conversion" of an RGB image and "image resizing and normalisation" to prevent inaccurate predictions. As the output layer of the proposed CNN architecture has two neurons with Soft max activation to classify the same, the suggested CNN then distinguishes between facial characteristics with and without masks. The suggested design has a validation accuracy of 96%. If anyone in the video a green rectangle is drawn around the appearance of a person using a mask, while a red rectangle with the words "NO MASK" is drawn around the face of stream.

Keywords : CNN Model, Java Script, APIs, Deep Learning, Tensor Flow, Keras, Open CV, Pandas, Performance, System Architecture, Adaptive Models.