It is vital to remain vigilant during pandemic COVID-19. Wearing a face mask is one of the crucial steps that people must take to ensure that they are a step away from spreading and infecting the virus. However, controlling and monitoring people in a densely crowded place is tough. Hence, a face mask detection system in public area is needed to remotely monitor if one is wearing a face mask or vice versa. In this study, two face masks datasets are downloaded from GitHub with 3834 images and 11800 colour images. Data pre-processing steps are carried out before the classification, which includes image resizing, converting images into array and label encoding. Two deep learning models, MobileNetV2 and VGG19, are developed for detection and evaluation. The experimental results performed by MobileNetV2 outperformed the VGG19 with achieving accuracy of 98.96% and 99.55% on Dataset 1 and Dataset 2 respectively.
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