Alert on Mask Detection System – Students Research in ML and DL at Durham College
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.
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- Date: 07 May 2022
- Time: 06:00 PM to 07:00 PM
- All times are (GMT-05:00) Canada/Eastern
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- Toronto, Ontario
- Canada
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Henil Shah
Alert on Mask Detection System – Students Research in ML and DL at Durham College
Speakers: Henil Shah, Neenu Markose
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