Face mask detection system - Kazi, Kutubuddin Sayyad Liyakat
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The Coronavirus Disease (Covid-19) was declared as a pandemic by WHO (World Health Organization) on 11 March 2020, and it is still currently going on, thereby impacting tremendously the whole world. As of September 2021, more than 220 million cases and 4.56 million deaths have been confirmed, which is a vast number and a significant threat to humanity. Although, as of 6 September 2021, a total of 5,352,927,296 vaccine doses have been administered, still many people worldwide are not fully vaccinated yet. As stated by WHO, "Masks" should be used as one of the measures to restrain the…mehr

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Produktbeschreibung
The Coronavirus Disease (Covid-19) was declared as a pandemic by WHO (World Health Organization) on 11 March 2020, and it is still currently going on, thereby impacting tremendously the whole world. As of September 2021, more than 220 million cases and 4.56 million deaths have been confirmed, which is a vast number and a significant threat to humanity. Although, as of 6 September 2021, a total of 5,352,927,296 vaccine doses have been administered, still many people worldwide are not fully vaccinated yet. As stated by WHO, "Masks" should be used as one of the measures to restrain the transmission of this virus. So, to reduce the infection, one has to cover their face, and to detect whether a person's face is covered with a mask or not, a "Face mask detection system" is needed. Face Mask Detection falls under the category of "Object Detection," which is one of the sub-domains of Computer Vision and Image Processing. Object Detection consists of both "Image Classification" and "Image Localization". Deep learning is a subset of Machine learning which, in turn, is a subset of Artificial intelligence that is widely being used to detect face masks.
Autorenporträt
Dr. Kazi Kutubuddin Sayyad Liyakat has completed his B.E., M.E., and Ph.D. in E&TC Engineering and is nowadays working as a Professor & Head of Department, E&TC Engineering Department. He is Post-Doctoral Fellow working on IoT in Healthcare Applications. His area of Interest is IoT, IoRT, IoBT, AI, ML, and AIIoT.