Masked Deep Face Recognition using ArcFace and Ensemble Learning

Abstract

With advancements in technology, human biometrics, especially face recognition, has witnessed a tremendous increase in usage, prominently in the field of security. Face recognition proves to be a convenient, coherent, and efficient way to identify a person uniquely. Face recognition systems are trained generally on human faces sans masks. With the ubiquitous use of face masks due to the ongoing COVID-19 pandemic, face recognition becomes a daunting challenge. In this paper, the deep learning architectures, namely MobileNetV2, DenseNet201, ResNet50V2, and VGG16 with the ArcFace loss function, were trained on the newly created dataset called "MaFaR", which consists of a mixture of masked and unmasked images of 75 distinct individuals, and ensemble learning techniques have been used to improve the performance, achieving an accuracy 93.65%.