Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras.
This project uses a Deep Neural Network, more specifically a Convolutional Neural Network, to differentiate between images of people with and without masks. The CNN manages to get an accuracy of 99.8% on the training set and 99.2% on the test set.
Then this CNN are used to classify as mask or no mask, using OpenCV.
The dataset we’ll be using here was created by Prajna Bhandary.
This dataset consists of 1,376 images belonging to two classes:
with_mask: 690 images without_mask: 686 images
Our model gave an accuracy of 99.8% on the training set and 99.2% on the test set.
We got the following accuracy/loss training curve plot
With Mask
No Mask
