Face mask detection using machine learning

Mohamed Wed Eladham; Ali Bou Nassif; Mohammad AlShabi

doi:10.1117/12.2672551

13 June 2023 Face mask detection using machine learning

Mohamed Wed Eladham, Ali Bou Nassif, Mohammad AlShabi

Proceedings Volume 12528, Real-Time Image Processing and Deep Learning 2023; 125280C (2023) https://doi.org/10.1117/12.2672551
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

The COVID-19 epidemic forced governments to adopt worldwide lockdowns in order to limit the virus's spread. Wearing a face mask, it is said, would reduce the possibility of transmission. Due to the growing urban population, proper city management is more important than ever in the modern day to reduce the impacts of COVID-19 infection. To check the mask in public places, however, would require incredibly long lineups and delays. Therefore, it is necessary for an autonomous mask detection system to assess whether someone is wearing a face mask. On the face mask dataset, three different machine learning methods are applied to determine the likelihood of wearing a face mask. The models were assessed using a number of measures, including accuracy, recall, and ROC curve. The main objective of the study is to detect the presence of face masks using deep learning, machine learning, and image processing approaches. All three models—NB, KNN, and CNN—achieved noteworthy accuracy of more than 80%, with CNN showing the best overall performance.

Citation Download Citation

Mohamed Wed Eladham, Ali Bou Nassif, and Mohammad AlShabi "Face mask detection using machine learning", Proc. SPIE 12528, Real-Time Image Processing and Deep Learning 2023, 125280C (13 June 2023); https://doi.org/10.1117/12.2672551

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