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DTSTAMP:20220507T235818Z
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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 t
 o make individuals wear face masks in public. As a result\, we require fac
 e detection systems that are both automated and efficient for such enforce
 ment. 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. Th
 e collected data set contains over 10\,000 images (considering 5\,000 with
  mask and similarly 5\,000 without) and has a 98 percent performance accur
 acy rate. The proposed model is computationally efficient and precise comp
 ared to Haar-Cascade & ANN. The application of this research are various\,
  including digitized scanning tool in schools\, hospitals\, banks\, airpor
 ts\, and many other public or commercial locations.\n\nSpeaker(s):  Henil 
 Shah\, \n\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.iee
 e.org/m/312341
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.o
 rg/m/312341
ORGANIZER:reza.dibaj@ieee.org
SEQUENCE:1
SUMMARY:Alert on Mask Detection System – Students Research in ML and DL a
 t Durham College
URL;VALUE=URI:https://events.vtools.ieee.org/m/312341
X-ALT-DESC:Description: <br /><p>As a result of the fast development and sp
 read of the COVID-19 pandemic throughout the world\, people's everyday liv
 es have been severely disrupted in recent times. One proposal for controll
 ing the epidemic is to make individuals wear face masks in public. As a re
 sult\, we require face detection systems that are both automated and effic
 ient for such enforcement. We propose a face mask identification model for
  static and real-time videos in this research\, and the pictures are class
 ified 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 (co
 nsidering 5\,000 with mask and similarly 5\,000 without) and has a 98 perc
 ent performance accuracy rate. The proposed model is computationally effic
 ient and precise compared to Haar-Cascade &amp\; ANN. The application of t
 his research are various\, including digitized scanning tool in schools\, 
 hospitals\, banks\, airports\, and many other public or commercial locatio
 ns.</p>
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