Masked Face Recognition Using MobileNetV2

Abstract

Masked face recognition has made great progress in the field of computer vision since the popularity of COVID-19 epidemic in 2020. In countries with severe outbreaks, people are required to wear masks in public. The current face recognition methods, which take use of the whole face as input data, are quite well established. However, while people are use of face masks, it will reduce the accuracy of face recognition. Therefore, we propose a mask wearing recognition method based on MobileNetV2 and solve the problem that many of models cannot be applied to portable devices or mobile terminals. The results indicate that this method has 98.30% accuracy in identifying the masked face. Simultaneously, a higher accuracy is obtained compared to VGG16. This approach has proven to be working well for the practical needs.