A review on face mask detection using image processing

Abstract

Face identification becomes an extremely challenging task, while wearing masks since some aspects of the face are obscured, making it crucial to develop advanced facial recognition technologies that can handle these complex scenarios effectively. The purpose of this paper is to offer an overview of several different approaches and algorithms that are utilized for human recognition when a face mask is applied. In this study, a number of distinct methodologies, including the Haar cascade, Adaboost, VGG-16 CNN Model, and others, are discussed. These approaches are compared side by side in order to determine which one has the best chance of success. It is possible that in the not too distant future, with the continued development of technology and the passage of time, more trustworthy methods for human recognition while wearing a face mask will come into use. It describes some of the practical uses of face detection and highlights the potential for advancements in reliable methods for human recognition even when individuals are wearing face masks. This technology has a variety of uses in public areas, schools, and other settings where persons need to be recognized despite wearing face masks in order to assist society. Facial recognition technology can also enhance security and streamline access control processes in a wide range of applications, from airports to corporate offices.