Performance Analysis of Three kinds of Neural Networks in the Classification of Mask Images

Abstract

The global spread of COVID-19 has led to a massive increase in public demand for effective measures to slow the spread of the virus. In response, the World Health Organization has advised people to wear face masks every day to stop the spread of the virus. In facial mask recognition, traditional manual feature extraction methods are cumbersome and inaccurate, considering the various types of masks and other practical problems. This paper considers various neural network models comprehensively and selects three classical CNN networks, including VGG, ResNet, and DenseNet. After using the data expansion and optimization methods, good data results are obtained for the three network models. Remarkably, the improved DenseNet accuracy can reach 99.55%.