Innovative approach of masked facial recognition

Not only diseases such as COVID-19 but also professions across healthcare, construction and manufacturing require the usage of facemasks.Currently, obstacles that are present in the available masked facial recognition techniques lack a focus on Indian specific databases which poses serious challenges in surveillance.In this paper, the proposed model, which achieved 96.7% accuracy, works in two-stages.The input goes through the preprocessing stage, which is based on an image cropping approach with a multitask cascaded convolutional neural network applied over it.Output moves to the convolutional neural network stage where the image is trained on two models, convolutional block attention module and modified face net architecture.Special techniques such as channel attention, spatial attention, batch normalisation, leaky rectification and face embedding vector normalisation, triplet selection are applied to reach an optimal level of accuracy.The robotic process automation engine using Bayesian optimisation is also put in place.