Deep learning strategies for accurate identification from facial composite images (E2ID)

Lead Participant: Visionmetric Limited

Abstract

"Facial composite images of criminal suspects (commonly known as PhotoFITs or EFITs) are routinely used by police forces to assist their criminal investigations throughout the world. However, the effectiveness of facial composites as tools of criminal investigation is severely limited by the current inability to accurately and rapidly search a population for potential matches to a composite image. Existing commercial face recognition systems do not adequately address this problem and perform very poorly on facial composite images.

In this project, we propose a radical, new approach to achieving fast and accurate matching of facial composite images to police suspect databases. We will use methods of artificial intelligence and advanced image processing to generate the world's largest repository of facial composite images. We will then exploit this resource to develop neural (deep learning) procedures that successfully map the human cognitive processes implicit in the recognition of facial composite images. In this way, machine behaviour will be tailored for the first time to achieve composite face recognition in a way similar to human-beings.

The expected developments will give rise to improved investigative procedures for international police forces leading to greater case closure and significant efficiency increases. A successful project will pave the way to commercially exploiting this technology in policing markets all over the world."

Lead Participant

Project Cost

Grant Offer

Visionmetric Limited, LEWES £341,367 £ 235,957
 

Participant

University of Greenwich, United Kingdom £24,099 £ 24,099

Publications

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