NeoFace is NEC's facial recognition algorithm, and it is the software behind most of what the United Kingdom calls police facial recognition. South Wales Police and the Metropolitan Police both use it, across all three of their use cases: live facial recognition at deployments, retrospective searching of recorded images, and operator-initiated facial recognition on an officer's handheld device. Where UK reporting refers to "the algorithm" without naming it, this is usually the one meant.

It is sold as a family rather than a single product. NeoFace Watch handles real-time CCTV matching, NeoFace M40 is the algorithm version South Wales Police names in its own public material, NeoFace V4 is the version the National Physical Laboratory evaluated, and NeoFace Nexus is the current platform NEC markets for investigative use. Version matters when reading accuracy claims, because they are version-specific.

HOW THE COMPANY DESCRIBES IT

NEC's own framing leans heavily on independent benchmarking. Its material states the technology has been proven by the US National Institute of Standards and Technology as the fastest and most accurate in the world, and as one of the most consistently equitable across demographics. It describes NeoFace as designed to find a needle in a haystack — reducing a large problem to a smaller review process — and as tolerant of ageing, angles, headwear and lighting.

The company also markets governance features rather than only performance: built-in privacy safeguards, auditing and reporting, and automatic and manual data-retention tools. NEC states it is guided by long-established internal principles on biometrics and AI encouraging ethical, proportionate and targeted use.

The NIST claim is genuinely substantiated in a way most vendor accuracy claims are not. NEC's algorithms have repeatedly placed among the top performers in NIST's Face Recognition Technology Evaluation for one-to-many identification, which is an independent benchmark the company does not control.

HOW IT IS USED IN POLICING

South Wales Police was the first UK force to deploy it, from July 2017, and the first anywhere in the UK to use real-time facial recognition at a large-scale sporting event. Cameras were mounted on police vehicles, streaming to the algorithm for matching against authorised watchlists of wanted people, suspects, missing persons and vulnerable individuals. The same system cross-checks crime scene images against the force's custody image database, then around 500,000 photographs.

NEC stated it had NeoFace Watch deployments in 47 countries at that point, a figure the company later put higher.

The three modes carry different intrusion profiles. Live facial recognition scans everyone passing a camera against a watchlist. Retrospective search takes an image after the fact and searches a custody database. Operator-initiated recognition lets an officer photograph a person in front of them and check that image. South Wales has used the handheld mode since 2024; the Met announced its own from December 2025.

THE ACCURACY EVIDENCE, AND WHAT IT ACTUALLY SHOWS

This is the best-evidenced algorithm in this catalogue, because the National Physical Laboratory published an operational evaluation in April 2023 rather than relying on laboratory benchmarks alone.

For retrospective and operator-initiated use the findings were strong: a true positive identification rate of 100%, with no significant difference by gender or ethnicity. Even critics of facial recognition policing have acknowledged this squarely — the campaign group StopWatch noted the finding addresses some of the concerns people hold about police use, while arguing accuracy is not the only question that matters.

For live facial recognition the picture is more conditional, and the condition is the threshold setting. NPL found that at the default 0.6 threshold the Met would see roughly one false alert in 6,000. At 0.64, the algorithm returned no false positives at all. But at 0.58 and 0.56, false positives rose — and crucially, they rose unevenly, with Black people incorrectly flagged as possible matches more often than people of other ethnicities.

South Wales Police says it typically operates at 0.62. Since the Court of Appeal judgment in Bridges, the force states testing confirmed its use does not discriminate on grounds of gender, age or race, and it resumed deployments on 5 April 2023. It publishes deployment dates and locations in advance except in exceptional circumstances, and states biometric data of anyone not on a watchlist is deleted immediately and automatically.

THE CASE FOR IT

The independent evidence base is the strongest argument, and it is unusual. Most systems in this catalogue rest on vendor claims; this one has been evaluated by a national measurement institute in operational conditions, and the results for two of its three modes were unambiguous.

The governance features are also real rather than rhetorical. Advance publication of deployment locations, immediate deletion of non-matches, and audit logging are meaningful controls, and their existence is verifiable from force material rather than only from marketing.

THE CASE AGAINST

The single most important finding is one neither the vendor nor most reporting foregrounds: equitable performance is a function of a threshold setting, and no law or formal policy requires any particular one. The same algorithm is demographically fair at 0.62 and demonstrably not at 0.56. A force can therefore comply with every published assurance about accuracy while operating at a setting where the disparity reappears, and the Met's own facial recognition lead has suggested lower thresholds might be appropriate in emergencies such as an ongoing terrorist attack — precisely the circumstances in which a wrongful match would be most consequential.

A 1-in-6,000 false alert rate also sounds small and is not, at scale. A deployment scanning tens of thousands of faces produces false alerts as a matter of routine, and each one is a person stopped by police on a machine's say-so.

There is a broader objection that accuracy improvements do not answer. A perfectly accurate system still enables identification of everyone passing a camera, and the question of whether that should happen is separate from whether the machine gets it right. StopWatch's argument is essentially this: fixing the algorithm addresses the least fundamental of the objections.

Face occlusion also degrades performance — masks, scarves, sunglasses, oblique angles — which is relevant given that watchlist subjects have obvious reason to obscure their faces.

WHAT IS NOT ESTABLISHED

Which threshold each UK force operates at, deployment by deployment, is not systematically published. South Wales states its usual figure; comprehensive disclosure across forces does not exist.

The NPL evaluation covers NeoFace V4. Forces have since referenced M40 and NEC now markets Nexus. Whether the published equitability findings carry across versions has not been independently re-established.

NEC's current deployment count in policing specifically is unclear. Figures of 47 and later 70 countries refer to all customers including airports and commercial operators, not to police forces.

No published data was found showing what proportion of NeoFace alerts across UK deployments resulted in an arrest, a charge or a wrongful stop, which is the outcome measure that would let anyone judge whether the accuracy figures translate into proportionate policing.