Convolutional Neural Networks for Face Detection and Face Mask Multiclass Classification

In recent years, due to the COVID-19 pandemic, there have been a large number of infections among humans, causing the virus to spread around the world. According to recent studies, the use of masks has helped to prevent the spread of the virus, so it is very important to use them correctly. Using masks in public places has become a common practice these days and if it is not used correctly the virus will continue to be transmitted. The contribution of this work is the development of a convolutional neural network model to detect and classify the correct use of face masks. Deep learning methods are the most effective method to detect whether a person is using a mask properly. The proposed model was trained using the MaskedFace-Net dataset and evaluated with different images of it. The Caffe model is used for face detection, after which the image is preprocessed to extract features. These images are the input of the new convolutional neural network model, where it is classified among incorrect mask, non-mask, and mask. The proposed model achieves an accuracy rate of 99.69% in the test percentage, which is higher compared to other authors.