A Deep Learning Approach for Face Mask Detection
Smart Innovation, Systems and Technologies, ISSN: 2190-3026, Vol: 286, Page: 295-303
2022
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
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Conference Paper Description
The whole world is passing through a very difficult time since the outbreak of Covid-19. Wave after wave of this pandemic hitting people very hard across the globe. We have lost around 3.8 million lives so far to this pandemic. Moreover, the impact of this pandemic and the pandemic-induced lockdown on the lives and livelihoods of the people in the developing world is very significant. Till now there is no one-shot remedy available to stop this pandemic. However, spread can be controlled by social distancing, frequent hand sanitization, and using a face mask in public places. So, in this paper, we proposed a model to detect face mask of people in public places. The proposed model uses OpenCv module to pre-process the input images, it then uses a deep learning classifier MobileNetV3 for face mask detection. The accuracy of the proposed model is almost 97%. The proposed model is very light and can be installed on any mobile or embedded system.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128917546&origin=inward; http://dx.doi.org/10.1007/978-981-16-9873-6_27; https://link.springer.com/10.1007/978-981-16-9873-6_27; https://dx.doi.org/10.1007/978-981-16-9873-6_27; https://link.springer.com/chapter/10.1007/978-981-16-9873-6_27
Springer Science and Business Media LLC
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