Face Matching for Automatic Identity Retrieval, Recognition, Verification and Management

在过去，当大多数人出生、生活和死亡都在同一个地方，每个人都彼此认识时，就不需要生物识别技术。然而，如今，随着社会快速向数字经济发展，以及国内和跨境人员流动达到前所未有的水平，基于生物识别技术的高效，稳健和有效的自动识别和验证个人的方法正在成为信息基础设施结构的基本要求和要素。为了促进商业和远程工作，为了访问智能城市的远程服务和物理站点，以及通过自动监控打击犯罪和恐怖主义，为更安全的社会做出贡献，都需要身份验证。在这种情况下，面部生物识别技术是一种首选的生物识别方式，因为它可以在不引人注目的情况下被捕获，即使受试者没有意识到被监控和可能被识别。它也是人类使用的模式，因此，在需要时，它支持机器和人类面部识别之间的无缝过渡和合作。尽管面部生物识别技术开始在多个领域得到应用，但目前仅限于对面部图像捕获过程施加严格控制的应用（受控照明下的正面面部识别）。然而，在非受控场景下的自动人脸识别是一个尚未解决的问题，因为在不同的姿势，不同的表情，在不同的照明下拍摄的图像中的人脸外观的可变性。此外，噪声、模糊和遮挡等退化现象加剧了图像的可变性。该项目将开发不受约束的人脸识别技术，该技术对一系列退化因素具有鲁强性，适用于数字经济和面临全球安全问题以及人口变化的世界。采用的方法将努力设计新颖的机器学习解决方案，将深度学习技术与3D面部模型传达的复杂先验信息相结合。科学上的挑战将是开发一种对各种成像因素不变的人脸图像表示。这将需要更好地理解自然面部外观变化和面部图像退化现象对面部图像表示的影响。这项工作将由萨里大学、伦敦帝国理工学院和斯特林大学这三个学术合作伙伴组成的多学科团队进行，他们在生物识别和面部建模方面拥有丰富的经验，并共同拥有必要的专业知识，包括人脸感知心理学。研究方向将定期重新评估，必要时进行修订，由代表生物识别行业、政府机构和无约束人脸识别技术潜在用户的外部专家团队提供指导。该项目的进展将通过广泛评估利用生物识别界设计的具有挑战性的基准测试开发的解决方案来衡量，并将其与不断发展的商业产品进行比较。

In the past, when the majority of people were born, lived and died in the same locality where everybody knew each other, there was no need for biometrics. However, nowadays, with the society moving rapidly towards Digital Economy, and the people mobility within the country and across borders reaching unprecedented levels, efficient, robust and effective ways of recognising and verifying individuals automatically, based on biometrics, is emerging as an essential requirement and element of the fabric of the information infrastructure. Identity verification is required to facilitate commerce, and remote working, to enable access to remote services and physical sites in smart cities, as well as contributing to a safer society by fighting crime and terrorism through automatic surveillance. In this context face biometrics is a preferred biometric modality, as it can be captured unobtrusively, even without subjects' being aware of being monitored and potentially recognised. It is also the modality used by humans and thus, when needed, it supports a seamless transition and cooperation between machine and human face recognition. Although face biometrics is beginning to be deployed in several sectors, it is currently limited to applications where a strict control can be imposed on the process of face image capture (frontal face recognition in controlled lighting). However, automatic face recognition in uncontrolled scenarios is an unsolved problem because of the variability of face appearance in images captured in different poses, with diverse expressions, under changing illumination. Furthermore, the image variability is aggravated by degradation phenomena such as noise, blur and occlusion. The project will develop unconstrained face recognition technology, which is robust to a range of degradation factors, for applications in the Digital Economy and in a world facing global security issues, as well as demographic changes. The approach adopted will endeavour to devise novel machine learning solutions, which combine the technique of deep learning with sophisticated prior information conveyed by 3D face models. The scientific challenge will be to develop a face image representation, which is invariant to various imaging factors. This will necessitate gaining better understanding of the effect of natural face appearance variations and face image degradation phenomena on face image representation. The work will be carried out by a multidisciplinary team constituted by three academic partners, University of Surrey, Imperial College London and University of Stirling, which has extensive experience in biometrics and face modelling, and jointly possesses the necessary expertise, including psychology of human face perception. The research direction will be regularly reappraised and if necessary revised, with steering provided by a team of external experts representing the biometrics industry, government agencies, and potential users of the unconstrained face recognition technology. The progress of the project will be measured by extensive evaluations of the solutions developed using challenging benchmarking tests devised by the biometrics community and compared with evolving commercial offerings.