In light of the novel Covid-19 pandemic, wearing masks has been declared mandatory in several institutions and public places for its widespread prevention and public health safety. Under given circumstances, person identification for security purposes including smart-phones face unlock has been a challenging task since the previous practices including both the human authentication by a person as well as by face recognition systems have heavily relied on complete facial features. However, the emergence of large datasets of masked images led to the rapid development of occluded face detection techniques. This paper focuses on single camera masked face detection and identification via the following two approaches: (i) single-step pre-trained YOLO-face/trained YOLOv3 model on the set of known individuals; and (ii) two-step process having pre-trained one stage feature pyramid detector network RetinaFace for localizing masked faces and VGGFace2 that generates facial feature vectors for efficient mask face verification. The dataset employed consists of real-world video examples comprising of 7 individuals with various orientations, illuminations, and occlusions. Experimental results show that RetinaFace and VGGFace2 achieve state-of-the-art results of 92.7% on overall performance, 98.1% face detection, and 94.5% face verification accuracy respectively in 1:1 face mask verification on our custom dataset.