DeepMask: face mask detection using GAN algorithm

COVID-19 pandemic is the main reason people must wear face masks in public places. Traditionally, officers monitor use of area manually. However, monitoring using manual techniques challenging a crowded spot. Thus, we propose mask detection based on Generative Adversarial Networks (GAN) through learning model to accelerate accurately and quickly. To construct our model, collect dataset, conduct pre-processing, train by tuning multiple parameters obtain highest accuracy tiny loss. The experimental results can produce D_Loss = 0.0032 G_Loss 7.3296. Therefore, proposed be promising solution for issues.