In the corona virus pandemic (COVID-19), It becomes strenuous to monitoring of people who cover up their faces continuously and those who alternately avoid covering up their faces. It cannot rely solely on human effort to perform this task and hence there is a need to evolve a shareware that has the capability to automatically detect an individual has covered up its face. This has become a very famous issue in visualisation and machine vision. Numerous advanced method were designed by make use of convolutional framework to train the method as flawlessly as practicable. These convolutional architectures built this feasible to draw out even significant feature of the pixels. The training is convey out through convolutional neural systems in order to semantically part of the faces that are here in this photo. Featuring observation and removal approach to assist us in identifying an individual having a mask on his face or not. Real time face mask revealer will utilize dataset of transformed covered up image data. As a result, the model created will be accurate and easily executable in a streamlined and built-in system in terms of computation as the mobile Netv2 architecture (Keras, Apsara, Google-colab, etc) will be unified. These frameworks can also be used in real-time shareware that require real-time detection of face masks for security reasons, due to the outbreak of the COVID-19 pervasive. The project can be integrate with on-board shareware systems at airports, railway stations, universities, colleges, bus stands, dockyards, malls and public places to make sure compliance with public safety recommendation. The above subject is extremely important recently, because the recognition exercise will not only help us categorize people, but further rapidly decrease the physical function of the person required to do so.