Classifiers Combination for Efficient Masked Face Recognition

Abstract

This study was developed following the upheaval caused by the spread of the Coronavirus around the world. This global crisis greatly affects security systems based on facial recognition given the obligation to wear a mask. This latter, camouflages the entire lower part of the face, which is therefore a great source of information for the recognition operation. In this article, we have implemented three different pre-trained feature extractor models. These models have been improved by implementing the well-known Support Vector Machines (SVM) to reinforce the classification task. Among the investigated architectures, the FaceNet feature extraction model shows remarkable results on both databases with a recognition rate equal to 90%on RMFD and a little lower on SMFD with 88.57%. Following these simulations, we have proposed a combination of classifiers (SVM-KNN) that would prove a remarkable improvement and a significant increase in the accuracy rate of the selected model with almost 4%.