Face Mask Classification based on Transfer Learning and MobileNet V2

ABSTRACT

Face mask image recognition can detect and monitor whether people wear the mask. Currently, the mask recognition model research mainly focuses on different mask detection systems. However, these methods have limited working datasets, do not give safety alerts, and do not work appropriately on masks. This paper aims to use the face mask recognition detection model in public places to monitor the people who do not wear the mask or the wrong mask to reduce the spread of Covid-19. The mask detection model supports transfer learning and image classification. Specifically, the collected data are first collected and then divided into two parts with_mask and without_mask. Then authors build, implement the model, and obtain accurate mask recognition models. This paper uses and size of images datasets tested respectively. The experimental results show that the effect of the image size of was relatively better, and the training accuracy of different MobileNetV2 models is about 95%. Our analysis demonstrates that MobileNetV2 can correctly classify Covid-19. © 2022 ACM.