A Real Time Prediction and Classification of Face Mask Detection using CNN Model

Abstract

Current scenario of COVID-19 (Corona Virus Disease) pandemic makes almost everyone to wear a mask in order to effectively prevent the spread of the virus. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on the masked faces. For that detecting the people with face mask is very essential. In this work, a reliable method based on discard masked region is proposed in order to address the problem of masked face recognition process. The first step is to discard the masked face region and extract the forehead and eyes region. Next, a pre-trained deep Convolutional neural networks (CNN) is applied to extract the best features from the obtained regions. Finally, it show experimental result is achieved an accuracy of more than 98% validation dataset.