Face mask recognition using a custom CNN and data augmentation

In 2019, the COVID-19 disease spread worldwide, and the World Health Organization recommended using masks for everyone. Using a mask is one of the ways to prevent the transmission of the coronavirus. Naturally, there was a need to distinguish people wearing masks from people without masks automatically. Artificial intelligence can be used to indicate masked from unmasked individuals if needed. In this regard, machine learning models and convolutional neural networks have been employed to design an effective model for mask recognition. This problem represents a supervised learning and binary classification problem, where one group is masked, and the other is unmasked. The proposed model has been implemented using the Python programming language, and PyTorch has been utilized for its development. The custom model includes four convolutional layers for extracting image features and four fully connected layers for the artificial neural network part, distinguishing a masked person from an unmasked person. A dataset of approximately 12,000 face mask recognition images was utilized for training the model, resulting in an accuracy of 99.95% during the training phase and 99.02% during the testing phase. In addition to the competitive accuracy achieved in training and testing, the proposed model has consistently demonstrated outstanding performance across multiple metrics. The average precision, recall, and F1 score, exceeding 99%, further highlight the exceptional capabilities of our model. These results are particularly notable compared to the findings reported in other articles, reaffirming the effectiveness and superiority of our proposed approach.