83956Masking the Truth: Effective facial recognition in a COVID-19 worldClosedCollaborative R&DInnovate UKThe 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.