Transfer Learning for Mobile Real-Time Face Mask Detection and Localisation

Mercaldo, Francesco; Santone, Antonella

OBJECTIVE: Due to the COVID-19 pandemic, our daily habits have sud-denly changed. Gatherings are forbidden and even when it is possible to leave the home, for health or work reasons, it is necessary to wear a face mask to reduce the possibility of contagion. In this context, it is crucial to detect violations by people who do not wear a face mask. MATERIALS AND METHODS: For these reasons, in this paper we introduce a method aimed to automatically detect whether people are wearing a face mask. We design a transfer learning approach by exploiting the Mo-bileNetV2 model to identify in images/video streams face mask violations. Moreover the proposed approach is able to localise the area related to the face mask detection with the relative probability. RESULTS: To asses the effectiveness of the proposed approach, we evaluate a dataset composed by 4095 images related to people wearing and not wearing a face mask, obtaining an accuracy of 0.98 in face mask detection. DISCUSSION AND CONCLUSION: The experimental analysis shows that the proposed method can be successfully exploited for the face mask viola-tions detection. Moreover we highlight that it is working also on device with limited computational capability and it is able to process in real time images and video streams, making our proposal able to be applied in the real-world.