Satisfied Facewatch customer, Simon Lawrence from Lawrence Garages spoke to Forecourt Trader about the positive impact that Facewatch is having on store safety as well as reducing stock loss and violent incidents.

 “Facewatch’s proactive approach to crime prevention sets it apart from conventional CCTV, and its effectiveness in ensuring worker safety aligns with the Lawrence Garages objectives. The combination of Facewatch with tailored safety measures has also significantly boosted employee morale, making staff feel safer and more empowered while at work.”

Click the link above to read the full article and contact us today to book your free demo and find out how Facewatch can help you prevent retail crime in your business:

“Shops & Robbers” A very compelling story on ITV News about the ‘warzone’ that shopkeepers deal with on a daily basis in the face of the “relentless rise” in violence and aggression on shopkeepers and their staff.

Click the link above to watch the full story and see a Facewatch customer share about their experience of using Facewatch to combat the issue of rising retail crime.

Contact us today to book your free demo and find out how Facewatch can help you prevent retail crime in your business:

Shoplifters speak to the BBC saying that “shoplifting was easy”.

The BBC report says that “Retailers have warned that shoplifting has become an “epidemic”. Nottinghamshire Police saw a 36% rise in shoplifting in the 12 months to July 2023. The British Retail Consortium (BRC) told the BBC that the levels of theft now cost retailers almost £1bn a year.”

Click the link above to read the full article and contact us today to find out how Facewatch can help you prevent retail crime in your business

Dave Hiscutt, Head of Operations for Bassett Retail spoke to Convenience Store about how his company have embraced AI in a concerted effort to reduce crime in their stores and protect their staff from abuse.
Facewatch was installed at their Londis Westham Road shop in June 2023 and with less than half the number of incidents reported in July compared to the previous month, they subsequently went on to install our technology in two other sites, labelling the move as the “biggest success”.
Click the link above to read the full article and contact us today to find out how Facewatch can help you

Source: The Telegraph
Click the link above to read the full article

The tech companies scanning faces as we shop

Facewatch featured in The Telegraph. Facewatch Founder Simon Gordon says AI tools simply will give stores a fighting chance against shoplifting gangs. “All we’re doing is supercharging shopkeepers’ ability to try to prevent crime in their store.”

#FacialRecognition #Facewatch  #CrimePrevention #TheTelegraph

Bonjour La France!
Facewatch filmed with @infofrance2 and Ambroise @ABouleis last week. Click the link above to watch the full report.To discuss how we can help protect your staff and customers, get in touch today! 0207 930 3225

#facialrecognition #crimeprevention #shoplifting


Facewatch recruit Super Recognisers as Facial Analysts to support AI.
As part of the effort to prevent crime and protect frontline retail workers, alongside two leading facial recognition algorithms, Facewatch use human experts to ensure that there is always human intervention and not just AI involved in decision making. “The combination of humans and AI means you get an incredibly accurate result”.  We actually achieve an accuracy rate of 99.97%.
Click the link above to read the full article in The Times. 


We are often asked about the accuracy of the Facewatch algorithm and what sets us apart from the others. It’s important to bear in mind that we are detecting faces in live conditions and achieving an accuracy rate of 99.87%.

Bias in terms of facial recognition relates to variation in accuracy, as quantified by false match rate or false non-match rate, across a multi-dimensional landscape of faces by age, gender, skin-tone and other facial attributes.

Previously, it was true that facial recognition algorithms had an issue with bias. However, in the past 5-10 years, software developers such as SAFR have focused their attention on combatting bias in facial recognition.

Modern algorithms have been trained on enough data to ensure that racial bias is pretty much eliminated.  Facewatch take this a step further by also using two algorithms, with the primary one being SAFR, and trained facial analysts who have been selected because they have been tested on disparate data sets of faces.

In the year 2000 a US Government Agency called the National Institute of Standards and Technology (NIST) established the Face Recognition Vendor Test (FRVT) program to evaluate the performance of face recognition algorithms which are submitted by software developers from around the world.

In 2019 NIST tested nearly 200 face recognition algorithms from nearly 100 developers with databases of more than 18 million images of more than 8 million people to quantify the demographics differences by sex, age, and race or country of birth. The results of these tests were published in the NIST FRVT Part 3 Demographic Effects report and found that most, but not all, of the algorithms did have a problem with bias which meant that false match rates were usually highest in African and East Asian people, and lowest in Eastern European individuals.

Conversely, for algorithms developed in China, this effect was reversed, with the lowest false positive rates on East Asian Faces, indicating that the training dataset for most algorithms was predominately made up of white faces apart from Chinese software developers whose datasets were predominantly made up of Chinese faces.

One important exception that the NIST noted in their report was that developers such as SAFR supplied identification algorithms “for which false positive differentials are undetectable”. What this shows is that the approach of curating a diverse and representative dataset does make it possible for software developers to train a facial recognition algorithm to have virtually no bias.

An AI or machine learning model can only be as good as the dataset it was trained upon and so with this in mind, from the outset SAFR curated a diverse and representative training dataset of high quality face images to train their SAFR facial recognition algorithms. They also continue to test and measure demographic differentials and further train their facial recognition algorithm to improve accuracy whilst maintaining low bias.





Facewatch Founder Simon Gordon recently spoke to CNN’s Anna Stewart about his reasons for starting Facewatch.

The full report can now be viewed here

Simon demonstrates how Facewatch works and provides an insight into how the company started from the point of view of a business owner who was experiencing rising levels of crime that the police didn’t have the resource to deal with.

Facewatch is the leader in facial recognition and is proven to reduce retail crime by up to 35% in 12 months.

Contact us today to find out how Facewatch is helping our subscribers to protect their staff and prevent crime

020 7930 3225

#cnn #facialrecognition #ai #crimeprevention



With reports of a new crime and justice bill that will require judges to impose custody on prolific shoplifters who are caught repeatedly, and the policing minister urging police forces to use facial recognition software, Facewatch Founder, Simon Gordon was invited to discuss how Facewatch helps to effectively combat retail crime on Jeremy Vine’s BBC Radio 2 show.


To listen to the full interview with Simon, as well as the listener response, the full episode is available here (Listen from 05:30)

To discover how Facewatch is effectively assisting our subscribers in safeguarding their staff and preventing crime, don’t hesitate to reach out to us today. You can find more information at here or contact us directly on 020 7930 3225.

#facialrecognition #facewatch #shoplifting #crimeprevention #BBCRadio2 #JeremyVine