Medical researchers around the globe provide evidence that COVID-19 pandemic diseases transmitted through droplets and respirators of respiratory aerosols and wearing a face mask is an efficient infection control recommendation process. In addition, many public and private service providers demand that consumers use the service only if they wear masks properly. However, a few research studies have been found on face mask detection based on the technology of Artificial Intelligence (AI) and Image Processing. In this article, we propose, MobileNet Mask, which is a deep learning-based multi-phase face mask detection model for preventing human transmission of SARS-CoV-2. Two different face mask datasets along with more than 5,200 images have been utilized to train and test the model for detecting with and without a face mask from the images and video stream. Experiment results show that with 770 validation samples MobileNet Mask achieves an accuracy of9 3% whereas with 276 validation samples it attains an accuracy of nearly~100%. Lastly, we also discuss the possibility of implementing our proposed MobileNet Mask model on light-weighted computing devices such as mobile or embedded devices. Besides, this proposed model also introduces frontier technologies to support the efforts of government and public health guidelines with anticipation of implementing mandatory face mask regulations all over the world.