Masked Face Detection and Recognition System in Real Time using YOLOv3 to combat COVID-19

Abstract

A new challenge in the facial recognition technology is observed during the COVID-19 pandemic which has created a need for developing alternatives in face recognition algorithms that exist today. During this pandemic, masked faces have made face recognition applications used for security surveillance and attendance systems less effective. A comparative study was conducted on different YOLO models like YOLOv3, Tiny-YOLOv3, Tiny-YOLOv4 to judge their performances for the face detection module. From the study, it is concluded that the YOLOv3 model outperformed the other algorithms. Additionally, the face images captured from cameras were encoded and compared to determine the best face images for the face recognition module. It was identified that YOLOv3 along with face encodings from IP camera images accomplished an overall testing accuracy of 95.83% on masked and unmasked faces. The system introduced a confidence level to further reduce the error while registering the identity of the person.