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

本项目的目的是开发一种新的人脸识别系统，能够准确可靠地对部分遮挡的人脸进行识别。新冠肺炎大流行的直接结果是对这一点的需求，面部覆盖不仅成为常见的地方，而且在许多情况下现在是强制性的。2020年7月，美国国家标准与技术研究所发布了一份报告，称在新冠肺炎出现之前开发的面部识别算法在准确识别戴面罩的人方面存在极大的困难。根据美国国家标准与技术研究所的说法：“在测试的89种商用人脸识别算法中，即使是最好的算法，在将数字应用的人脸面具与同一个不戴面具的人的照片进行匹配时，错误率也在5%到50%之间。”在这个项目中，我们将实现一个面部识别系统，该系统是；*通用。这意味着它将能够有效地对任何图像进行分类。*可以在低功耗硬件上运行。这意味着它将是节能的，并限制温室气体(温室气体)排放。*能够自我指导的动态学习。这意味着它将能够在工作中学习。*没有学科偏见。具有这些属性的识别系统将是颠覆性的，改变游戏规则，并明显超过最先进的水平。

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.