Masked Facial Recognition in Security Systems Using Transfer Learning

The COVID-19 is a crisis of unprecedented magnitude, which has resulted in countless casualties and security troubles. In view of recent events of corona virus people are required to wear face masks to protect themselves from getting infected. As a result, a good portion of face (nose and mouth) is hidden by the mask and hence the facial recognition becomes difficult. Many organizations use facial recognition as a means of authentication. Researchers focus on developing rapid and efficient solutions to deal with the ongoing coronavirus pandemic by coming up with suggestions for handling the facial recognition problem. This research paper aims to identify the person, while the face is covered with a facial mask with only eyes and forehead being exposed. The first step involves marking the facial region. Next, using the data set, we will implement an object detection model YOLOv3 to identify unmasked and masked faces. The YOLO v3 object detection model is the best performing model with a detection time of 0.012 s, F1 score of 0.90 and mAP score of 0.92. Experimental results on Real-World Masked-Face-Data set show high recognition performance.