Masking the Truth: Effective facial recognition in a COVID-19 world

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The aim of this project is to develop a new facial recognition system, one which can perform accurately and reliably on partially occluded faces. The need for this has arisen as a direct result of the COVID-19 pandemic, where facial coverings have not only become common place but in many instances are now compulsory.In July 2020, the US National Institute of Standards and Technology (NIST) published a report which stated that facial recognition algorithms developed before the emergence of COVID-19 have "_great difficulty_" in accurately identifying people wearing facial coverings.According to NIST: "_Even the best of the 89 commercial facial recognition algorithms tested had error rates between 5% and 50% in matching digitally applied face masks with photos of the same person without a mask._"During this project we will implement a facial recognition system which is;* Generic. Meaning it will be capable of efficiently classifying any image.* Can be run on low power hardware. Meaning it will be energy efficient and limit GHG (greenhouse gas) emissions.* Capable of self-directed dynamic learning. Meaning it will be able to learn 'on the job'.* Shows no subject bias.A recognition system with these attributes will be disruptive, game changing and a clear advance over the state-of-the-art.