Multi-Class Image Classification Based on MobileNetV2 for Detecting the Proper Use of Face Mask

Abstract

Wearing face mask in public has become a health protocol standard during this pandemic to prevent further spread of COVID-19. Even though the detection of inappropriate use of face mask is important considering that people sometimes ignore the health protocols by lowering their face mask so it does not cover their nose, studies regarding automatic detection of proper use of face mask are still few. Therefore, in this research we propose a multi-class image classification for detecting the proper use of face mask based on MobileNetV2 architecture as the base model. We also propose a trainable head model for the network, consisting of a depthwise convolution layer and two fully-connected layers, that gives high classification performance. The experimental results show that the proposed system gives a high multi-class classification performance with an accuracy of 97%, precision of 97%, recall of 97%, and F1-score of 97%. The running time of the proposed method is 265.94 seconds which is considered efficient compared with other models. Because of its light-weight network architecture, the proposed method is suitable for further implementation towards a real-time application of surveillance systems. Therefore, in this research we present the results of an initial experiment of the proposed model on a real-time detection system by using a web camera.