Facial mask detection

Face Detection has evolved as a very popular problem in Image processing and Computer Vision. Many new algorithms are being devised using convolutional architectures to make the algorithm as accurate as possible. These convolutional architectures have made it possible to extract small details. We aim to design a binary face classifier which can detect any face present in the frame of its alignment. Beginning from the RGB image of any size, the method uses an grayscale image from camera. Training is performed through Fully Convolutional Networks to semantically segment out the faces present in that image. Gradient Descent is used for training while Binomial Cross Entropy is used as a loss function. Further the output image from the FCN is processed to remove the unwanted noise and avoid the false predictions if any and make bounding box around the faces. Face Mask Detection system built with OpenCV, Keras/Tensor Flow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time videostreams.