An Automated Face Mask Detection System using Deep CNN on AWS Cloud Infrastructure

Face detection methods have usually been used mostly for faces that aren’t covered up (non-masked faces). These include nose, lips, ears, chin, and eyes. Faces must be concealed by masks in various locations, including labs, hospitals, crime scenes, and areas with high levels of pollution. The global spread of coronavirus known as COVID-19 has disastrous effects on community wellbeing. This study aims to apply a model too complicated data, namely, image and real-time video image tasks for personal face identification with and without a mask. The healthcare industry is in a hazardous state. Numerous preventative measures, including the use of masks, are being set in place to restrict the spread of this sickness; WHO (World Health organization) highly encourages the use of a mask. This extremely large number of COVID-19 samples provides valuable information regarding the pandemic’s progression, as well as a possible way to encircle it to prevent future transfers. To prevent the transmission of the virus, several preventative measures have been implemented, including the use of masks. using a face mask that prevents droplet transfer discharged into the atmosphere, on either hand, may help avoid the outbreak. As a solution, the purpose of this function will be to form an FMD (Face Mask Detection) model which can be used as part of an embedded imaging system. It is expected to enhance the detection rate and achieve an accuracy of more than 99%.