Automated Face Mask Detection using Pretrained CNN
Keywords:
CNN, COVID-19, Face Mask Detection (FMD), SARSAbstract
In recent times, the use of face masks has emerged as a critical subject. Automated facial mask detection holds the potential to curb the transmission of the COVID-19 virusand SARS-VIRUS within communal areas through the identification of individuals who are not utilizing masks. In this work, a pretrained Convolutional Neural Network (CNN), ResNet-50 utilized, which was initially trained on the ImageNet competition data. This model is augmented with a 300-linear layer network ,and fine-tuned on a dataset that is well-balanced comprising 1,000 facial images. During the evaluation of the validation dataset consisting of approximately 800 face images, the model achieved an impressive 99% accuracy. Its primary objective is to ascertain if an individual is wearing a facial mask using a cropped image of their face. By leveraging such advanced technologies, we can contribute significantly to public health and safety measures in the ongoing battle against COVID-19 and SARS-VIRUS.
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