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Face-mask-Detection-1

Inspiration

  • COVID-19 is an infectious disease caused by the SARS-CoV2 virus, but anyone can become seriously ill from COVID-19, regardless of their age.
  • When an infected person coughs, sneezes, speaks, sings, or breathes, small liquid particles spread the virus.
  • Having a mask on is an effective way to reduce the spread of the COVID-19 virus. This project will assist local authorities in monitoring public areas for public safety.

What it does

  • The task of ensuring everyone wears a facemask is not an easy one.
  • The application recognizes human faces and determines whether they are wearing face masks or not.
  • Face recognition and mask detection is performed in real-time in this application.

How we built it

  • It contains 10000 images for training and 400 images for validation taken from the Kaggel website
  • As the dataset used consists of images with different colors, sizes, and orientations, the images are converted to grayscale and normalized.
  • Data is augmented to provide robustness for the model and to avoid overfitting.
  • A CNN model is then built over the processed data. It is found that the model is 97.5% accurate
  • Using openCV, the model was tested in real-time and deployed over the web.

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