since the COVID-19 global epidemic, the usage of facial masks in public places has become a societal duty, and the identification of facial masks might be critical to protect public safety. Face mask recognition in video footage is a difficult task, owing to the fact that the masks themselves act as occlusions to face detection algorithms due to the lack of facial landmarks in the masked regions. In this paper, we offer a deep learning-based method for recognizing facial masks in films. The suggested framework makes use of the MTCNN face detection model to recognize faces and the facial landmarks that correspond to them that are visible in the video frame. A neoteric classifier uses the MobileNetV2 architecture as an object detector for finding masked regions to analyze these facial images and cues. The suggested framework was evaluated using a dataset made up of films documenting how individuals move about in public areas while adhering to COVID-19 safety standards. By achieving excellent precision, recall, and accuracy, the suggested methodology demonstrated its efficacy in identifying facial masks.