For the past two years, the rapid spread of pandemic viruses such as COVID-19 has been significant threat to health of people all around the world. The direct touch that people have with one another is one of the primary factors that contributes to the rapid spread of this illness. Despite the fact that there are numerous preventative steps that can be taken for lessen the transmission of any virus, most significant one is to utilize face masks when you are in communal places. The identification of face-masks in communal places is significant obstacle that must be conquered to lessen likelihoods of virus being passed from person to person. Using deep learning-DL algorithms, framework for face-mask identification has been proposed as a means of efficiently controlling the spread of this deadly disease. This system is intended to meet the issues that have been presented. For the purpose of achieving effective face mask identification, this work makes use of transfer learning models that are built on deep convolution neural networks (DCNN). On our dataset, we conducted an analysis to determine how well these models operate. Our dataset is split into two fragments: training dataset and testing dataset. 10% of data is utilised for testing, whereas 90% of data is used for training. The accuracy of the method that has been proposed is not less than 95%.