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.

Speaker(s): Henil Shah, Neenu Markose

Register: https://events.vtools.ieee.org/m/312341