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DTSTART;TZID=America/New_York:20220507T180000
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UID:10000361-1651946400-1651950000@www.ieeetoronto.ca
SUMMARY:Alert on Mask Detection System – Students Research in ML and DL at Durham College
DESCRIPTION:As a result of the fast development and spread of the COVID-19 pandemic throughout the world\, people’s everyday lives have been severely disrupted in recent times. One proposal for controlling the epidemic is to make individuals wear face masks in public. As a result\, we require face detection systems that are both automated and efficient for such enforcement. We propose a face mask identification model for static and real-time videos in this research\, and the pictures are classified as “with mask” or “without a mask.” The model uses a Kaggle dataset to train and test. The collected data set contains over 10\,000 images (considering 5\,000 with mask and similarly 5\,000 without) and has a 98 percent performance accuracy rate. The proposed model is computationally efficient and precise compared to Haar-Cascade & ANN. The application of this research are various\, including digitized scanning tool in schools\, hospitals\, banks\, airports\, and many other public or commercial locations. \nSpeaker(s): Henil Shah\, Neenu Markose \nRegister: https://events.vtools.ieee.org/m/312341
URL:https://www.ieeetoronto.ca/event/alert-on-mask-detection-system-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312341
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
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