Face Mask Detection using CNN

Saranya, G.; Sarkar, Dipshikha; Ghosh, Sayan; Basu, Lokenath; Kumaran, K.; Ananthi, N.

doi:10.1109/csnt51715.2021.9509556

Cited by 9 publications

(1 citation statement)

References 6 publications

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“…Accuracy. F1-Score is used to evaluate the model's performance in the test set [10]. A total of 7 2.6.6.…”

Section: Msementioning

confidence: 99%

Using Convolutional Neural Network for Detection of Face Mask

Yang

2023

ACE

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The COVID-19 pandemic has had a sweeping impact across the globe, resulting in enormous economic losses and significant changes to people's way of life. Despite the World Health Organization's (WHO) assertion that the COVID-19 pandemic will conclude by 2023 and people's lives will begin to settle down, the possibility of a resurgence of the virus cannot be overlooked. In crowded public places, it is essential to have a system that can rapidly and accurately detect whether people are wearing masks and adhering to proper usage protocols. Therefore, it is crucial to make necessary preparations for such a system. Fortunately, there have been notable advancements in facial recognition technology, which can aid in this endeavor. This paper aims to build a model for face-mask-detection with convolutional neural network to help perform a rapid mask-wearing check and carry out the system model training with a final accuracy reaching 0.95. In the end, high accuracy is observed in the classification of correctly wearing masks and incorrectly wearing masks, demonstrating that the model is capable to identify whether people wear masks and wear correctly with the final accuracy reaching 0.97.

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“…Accuracy. F1-Score is used to evaluate the model's performance in the test set [10]. A total of 7 2.6.6.…”

Section: Msementioning

confidence: 99%