Performance Evaluation of Face Recognition System (FRS) Developed with Haar Cascade and MongoDB Integration in Recognition of Covered Faces

Abstract

Although traditional face recognition systems (FRS) can detect with a certain success rate whether a mask is worn, they may fail due to the fact that most of the faces of the people who wear masks are covered. The difficulties arising from the fact that a significant part of the faces of individuals wearing masks are covered limits the performance of existing FRSs. In this research, it is aimed to integrate the Haar Cascade method with the MongoDB database in real time using the OpenCV library for mask-wearing face recognition and to demonstrate its performance with extensive experiments. In the experiments, the data set created within the scope of this study from realistic face images, in which most of the masked faces are covered, was used. Our research has shown that the accuracy of face recognition is 85% for masked faces, 61% for unmasked faces, and 41% when half of the face is covered by a different object. It is considered that this study will contribute to the literature in terms of providing a more effective and applicable mask detection solution by combining the Haar Cascade method with real-time database management integration.