I Mplementation of Machine Learning Techniques for Face Mask Detection

The global pandemic's pervasiveness has impacted human life severely. The COVID-19 pandemic had a devastating effect which resulted in a huge amount of loss for people financially, mentally as well as physically. This is because the disease rapidly disseminated all over the globe which in turn resulted in the rise of the infection rates. Many techniques have been devised to control the spread of the virus such as washing the hands frequently, maintaining a distance from people in public places, wearing mask. Out of all the techniques mentioned above, wearing a mask has been proven as the most efficient technique. Thus, the administrative agencies have been insisting the citizens to wear masks, and law enforcement agencies have been consistently keeping a check on the citizens so that each individual follows this instruction. As a result, there is a need to provide solution in the form of techniques for automatically detecting masks on individual faces which in turn will also help the law-enforcement agencies and governments so as to reduce the spread of the pandemic. This research makes use of image processing approaches to achieve the goal of providing a solution for face mask detection. In this paper, the methodology employs a convolution neural network and a decision tree to achieve accurate facial mask detection. Frame Capturing, Pre- processing, Region of Interest, Convolutional Neural Network, and Decision Tree are the five modules included in the proposed methodology.