CNN-based bi-directional and directional long-short term memory network for determination of face mask

CONTEXT: The COVID-19 virus, exactly like in numerous other diseases, can be contaminated from person to person by inhalation. In order to prevent the spread of this virus, which led to a pandemic around the world, a series of rules have been set by governments that people must follow. The obligation to use face masks, especially in public spaces, is one of these rules. OBJECTIVE: The aim of this study is to determine whether people are wearing the face mask correctly by using deep learning methods. METHODS: A dataset consisting of 2000 images was created. In the dataset, images of a person from three different angles were collected in four classes, which are “masked”, “non-masked”, “masked but nose open”, and “masked but under the chin”. Using this data, new models are proposed by transferring the learning through AlexNet and VGG16, which are the Convolutional Neural network architectures. Classification layers of these models were removed and, Long-Short Term Memory and Bi-directional Long-Short Term Memory architectures were added instead. RESULT AND CONCLUSIONS: Although there are four different classes to determine whether the face masks are used correctly, in the six models proposed, high success rates have been achieved. Among all models, the TrVGG16 + BiLSTM model has achieved the highest classification accuracy with 95.67%. SIGNIFICANCE: The study has proven that it can take advantage of the proposed models in conjunction with transfer learning to ensure the proper and effective use of the face mask, considering the benefit of society.