The Metropolitan Police have announced positive results from their study into the use of facial recognition technology to help fight crime. 

A study has been taken into Live Facial Recognition by the National Physical Laboratory and has found that the chance of having an incorrect match using the technology is 0.017% or that the chance of a false match is just one out of every 6000 people walking past the camera. 

The technology will enable a more focussed approach to tackling crime, including looking into robbery and violence against women and girls. 

Lindsey Chiswick, Director of Intelligence for the Met said: "Live Facial Recognition technology is a precise community crime fighting tool. Led by intelligence, we place our effort where it is likely to have the greatest effect.  

"It enables us to be more focused in our approach to tackle crime, including robbery and violence against women and girls. 

"This is a significant report for policing, as it is the first time we have had independent scientific evidence to advise us on the accuracy and any demographic differences of our Facial Recognition Technology. 

"We commissioned the work so we could get a better understanding of our facial recognition technology, and this scientific analysis has given us a greater insight into its performance for future deployments. 

"We know that at the setting we have been using it, the performance is the same across race and gender and the chance of a false match is just 1 in 6000 people who pass the camera. All matches are manually reviewed by an officer. If the officer thinks it is a match, a conversation will follow to check. 

"The study was large enough to ensure any demographic differences would be seen. However, he has also been able to extrapolate these figures to reflect results more representative of watch list size for previous LFR deployments." 

The study also found that when the technology was used at a threshold setting of 0.6 or above, there was not any difference statistically significant difference in matches across groups, meaning the performance was the same across different genders and races. 

Chiswick said: "We understand the concerns raised by some groups and individuals about emerging technology and the potential for bias.  

"We have listened to these voices. This research means we better understand the performance of our algorithm.  

"We understand how we can operate to ensure the performance across race and gender is equal."