Enhanced Facial Recognition Techniques for Masked Individuals Amid the COVID-19 Pandemic

Abstract

The use of facemasks has been recommended by the World Health Organization (WHO) as an effective protective measure against the transmission of infectious diseases, such as COVID-19, in public spaces. Consequently, certain service providers require clients to wear masks before accessing their services. In this study, a novel facial recognition method is developed to identify individuals wearing medical facemasks in images. The proposed technique combines Convolutional Neural Networks (CNNs) to extract prominent feature characteristics, primarily from the eye and forehead regions of the face, and a facemask classification approach utilizing IInceptionV3, VGG16, VGG19, ResNet50, and MobileNet algorithms. A comparison between the five classifiers is also conducted to determine the most suitable algorithm for two masked face datasets. The VGG19 model outperforms the other models in terms of accuracy for the larger dataset. The proposed method achieves a precision of 98%, an average recall of 98%, an F1_score of 98%, and an overall accuracy of 98%. Therefore, the larger dataset yields higher accuracy, and the overall performance of the models is superior compared to the smaller dataset.