Coronavirus disease (COVID) is an unprecedented crisis, causing a huge amount of unhappiness and security problems. Wearing a face mask in public places can effectively reduce the transmission of coronavirus, so people should wear facial covers or masks to protect themselves from this pandemic. Therefore, this makes a facial confirmation an inconvenient task since obvious parts of the face are hidden. An important focus of analysts during the advancing COVID pandemic is to come up with ideas to address this problem with quick and productive measures. This paper proposes a reliable method based on hidden area removal and deep learning-based highlights to solve the problem of hidden face recognition measures. To deal with these challenges, it separates two unique usages in particular, such as closed-eye face detection and hidden face detection. It basically determines if a person has a mask on their face or not. It can be effectively applied transparently where a mask is needed. In contrast, the covered face affirmation means recognizing the presence of a mask on the face based on the eye area and shelter areas, as well as checking the temperature to ensure safety measures. This paper provides a survey of different research accomplished with the methodology on face mask detection with temperature check.