UK firm is first overseas company to win a US border security contract

London, UK

London cyber security firm Iproov has won a contract to help the US Department of Homeland Security to accurately and reliably identify travellers at airport checkpoints and other border crossings.

Iproov is a facial recognition business supported by Innovate UK, and is the first overseas company to win a contract to tackle border security in the United States. Innovate UK is part of UK Research and Innovation, a non-departmental public body funded by a grant-in-aid from the UK government

Iproov’s facial recognition technology is able to detect so-called ‘spoofs’ or people trying to trick biometric sensors using a fake photo, screen image or doctored video.

It will be used to speed up border crossings at unmanned ports of entry. Travellers will be able to self-identify on a mobile phone by authenticating against a pre-registered photo.

Andrew Bud, chief executive of Iproov, believes that advances have led to growth in face recognition, and he said: "We are delighted that Iproov has been selected by the US Department of Homeland Security to enhance the way in which it processes people through US borders."

"Advances in machine learning and artificial intelligence have enabled a revolution in facial biometrics in the last few years. We’re now seeing more and more cases of governments and banks turning to self-service, spoof-resilient face verification as the biometric of choice to both increase security and ensure ease of use."

Iproov’s facial biometric technology is used by banks and governments across the world to securely authenticate customers and guard against fraud.

The company was founded in 2011 and employs 20 people. It has previously presented its technology at Finovate Europe - the conference for banking, financial and payments technology - where it won a Best in Show award.

It has had funding support from Innovate UK for a number of projects. These include ways to counter impersonation, proving how well its systems capture identities even in difficult cases, improving the performance of facial recognition and tackling cyber attacks on face recognition technology.


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