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

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.

The proposed EPSRC programme FACER2VM will generate highly visible scientific, economic and societal impact.
Advancement in the understanding of the process of human and machine face matching, and in particular how to model natural face appearance variations and how to handle sensor signal degradation phenomena will contribute to scientific knowledge with wider implications on machine perception. The scientific impact will be enhanced by vigorous dissemination activities through top ranking journal and conference publications.

The scientific achievements will impact on education and training through the partner institutions' research led postgraduate degree programmes and their continuous professional education offerings. It will also contribute to training highly skilled researchers and engineers for the national economy.

Effective solutions to unconstrained face matching will enable the development of new applications and services of biometrics technology in Digital economy, which will foster economic impact. The expected results of the programme grant will help to enhance UK industry competitiveness in a sector forecast by TechNavio to grow in the facial recognition market alone at a CAGR of 26.6% during the period 2013-2018. The commercial impact will be promoted by the industrial partners with keen interest in exploitation of advanced face recognition technology to be developed by the project. An important role in the impact generation will be played by the European Association for Biometrics as a channel to its 65 industrial members with interests in biometrics.

The societal impact is anticipated to be multifaceted. Unconstrained face biometrics capability will significantly contribute to the government's security agenda in the framework of smart cities and national security. It can effectively facilitate access to public services. The impact will be maximized by the involvement of the Home Office CAST, and of Working Group on Biometrics in the FACER2VM user group, an organization representing diverse users of biometric technology in government departments.

Face recognition technology also has diverse applications aiming at enhancing the quality of life of individuals, such as facilitating the personalization of devices and person specific monitoring of individuals in smart homes for all, but especially for the benefit of the aging society.

The outreach element of FACER2VM will contribute to attracting new generations to careers in knowledge economy in general, and science and engineering in particular.