Fast R-CNN based Masked Face Recognition for Access Control System

Masked face recognition is becoming an essential requirement in most of the facial recognition based access control and authentication systems, particularly after the Covid-19 pandemic. The work analyses the capability of Region Based Convolutional Neural Networks (R-CNN) for masked face detection and demonstrates a facial recognition system with R-CNN in hardware. R-CNN uses Region Proposal Networks (RPN) that can extract non-occluded region on an image and feed it to a Deep Neural Network for recognition. The R-CNN classifier running on the region containing the non-occluded part of the face will be used for classification in case of a masked face. In case of recognition of unmasked face, the classifier will be run on the region containing the face. By this way, the system will be able to recognize face for both cases. Python modules like opencv, numpy have been used for image pre-processing, while Tensorflow has been used for image classification. Custom dataset is used for training. The trained deep learning model is evaluated using a confusion matrix heat-map which can be used to know the reliability of the model. The demonstration system consists of a Raspberry Pi module connected to a door actuator and a camera. On successful authentication, the system opens the door.