In the COVID-19 crisis wearing masks is absolutely necessary for public health and controlling the spread of the pandemic. What if we had a system that could monitor whether people around us are complying with these safety measures?
libraries needed
tensorflow
Keras
cv2
Numpy
sklearn
matplotlib
Pillow PIl
I have made use of Transfer Learning and thanks to the tensorflow applications api this is a very simple task. The MobileNetV2 model was used to build my classifier network.By using Transfer Learning I am making use of the feature detection capabilities of the pre-trained MobileNetV2 and applying it to our rather simple model. The MobileNetV2 is followed by our DNN composed of GlobalAveragePooling, Dense and Dropout layers. As ours is a binary classification problem final layer has 2 neurons and softmax activation. A general Adam optimizer along with Categorical_crossentropy loss works well to converge on the most optimum weights for my network. Here, I have used the popular OpenCV library to take my webcam feed and run it through my model.for face detection cvlib library is used along with method "Detect_face". The OpenCV implementation is simply a continuous cycle of: Detect Face Slice Face Image Pass through Classifier Get and Display Prediction
Results :
Accuracry : 99%
Val_Accuracy : 98.6
loss: 0.0166
val_loss: 0.0222
