A big biometric security company in the UK, Facewatch, is in hot water after their facial recognition system caused a major snafu - the system wrongly identified a 19-year-old girl as a shoplifter.
A big biometric security company in the UK, Facewatch, is in hot water after their facial recognition system caused a major snafu - the system wrongly identified a 19-year-old girl as a shoplifter.
Ok, some context here from someone who built and worked with this kind tech for a while.
Twins are no issue. I’m not even joking, we tried for multiple months in a live test environment to get the system to trip over itself, but it just wouldn’t. Each twin was detected perfectly every time. In fact, I myself could only tell them apart by their clothes. They had very different styles.
The reality with this tech is that, just like everything else, it can’t be perfect (at least not yet). For all the false detections you hear about, there have been millions upon millions of correct ones.
Twins are no issue. Random ass person however is. Lol
Yes, because like I said, nothing is ever perfect. There can always be a billion little things affecting each and every detection.
A better statement would be “only one false detection out of 10 million”
You want to know a better system?
What if each person had some kind of physical passkey that linked them to their money, and they used that to pay for food?
We could even have a bunch of security put around this passkey that makes it’s really easy to disable it if it gets lost or stolen.
As for shoplifting, what if we had some kind of societal system that levied punishments against people by providing a place where the victim and accused can show evidence for and against the infraction, and an impartial pool of people decides if they need to be punished or not.
100%
I don’t disagree with a word you said.
FR for a payment system is dumb.
Another way to look at that is ~810 people having an issue with a different 810 people every single day assuming only one scan per day. That’s 891,000 people having a huge fucking problem at least once every single year.
I have this problem with my face in the TSA pre and passport system and every time I fly it gets worse because their confidence it is correct keeps going up and their trust in my actual fucking ID keeps going down
Interesting. Can you elaborate on this?
Edit: downvotes for asking an honest question. People are dumb
Or ever, because it locks you out after a drunken night otherwise.
Or ever because there is no such thing as 100% in reality. You can only add more digits at the end of your accuracy, but it will never reach 100.
This makes it sound like you only tried one particular set of twins–unless there were multiple sets, and in each set the two had very different styles? I’m no statistician, but a single set doesn’t seem statistically significant.
What I’m saying is we had a deployment in a large facility. It was a partnership with the org that owned the facility to allow us to use their location as a real-world testing area. We’re talking about multiple buildings, multiple locations, and thousands of people (all aware of the system being used).
Two of the employees were twins. It wasn’t planned, but it did give us a chance to see if twins were a weak point.
That’s all I’m saying. It’s mostly anecdotal, as I can’t share details or numbers.
No, it gave you a chance to see if that particular set of twins was a weak point.
With that logic we would need to test the system on every living person to see where it fails.
The system had been tested ad nauseum in a variety of scenarios (including with twins and every other combination you can think of, and many you can’t). In this particular situation, a real-world test in a large facility with many hundreds of cameras everywhere, there happened to be twins.
It’s a strong data point regardless of your opinion. If it was the only one then you’d have a point. But like I said, it was an anecdotal example.
Can you please start linking studies? I think that might actually turn the conversation in your favor. I found a NIST study (pdf link), on page 32, in the discussion portion of 4.2 “False match rates under demographic pairing”:
This seems to say that the false match rate gets higher and higher as the subjects are more demographically similar; the highest error rate on the heat map below that is roughly 0.02.
Something else no one here has talked about yet – no one is actively trying to get identified as someone else by facial recognition algorithms yet. This study was done on public mugshots, so no effort to fool the algorithm, and the error rates between similar demographics is atrocious.
And my opinion: Entities using facial recognition are going to choose the lowest bidder for their system unless there’s a higher security need than, say, a grocery store. So, we have to look at the weakest performing algorithms.
My references are the NIST tests.
https://pages.nist.gov/frvt/reports/1N/frvt_1N_report.pdf
That might be the one you’re looking at.
Another thing to remember about the NIST tests is that they try to use a standardized threshold across all vendors. The point is to compare the results in a fair manner across systems.
The system I worked on was tested by NIST with an FMR of 1e-5. But we never used that threshold and always used a threshold that equated to 1e-7, which is orders of magnitude more accurate.
This definitely is a massive problem and likely does contribute to poor public perception.
Thanks for the response! It sounds like you had access to a higher quality system than the worst, to be sure. Based on your comments I feel that you’re projecting the confidence in that system onto the broader topic of facial recognition in general; you’re looking at a good example and people here are (perhaps cynically) pointing at the worst ones. Can you offer any perspective from your career experience that might bridge the gap? Why shouldn’t we treat all facial recognition implementations as unacceptable if only the best – and presumably most expensive – ones are?
A rhetorical question aside from that: is determining one’s identity an application where anything below the unachievable success rate of 100% is acceptable?
It’s a good question, and I don’t have the answer to it. But a good example I like to point at is the ACLU’s announcement of their test on Amazon’s Rekognition system.
They tested the system using the default value of 80% confidence, and their test resulted in 20% false identification. They then boldly claimed that FR systems are all flawed and no one should ever use them.
Amazon even responded saying that the ACLU’s test with the default values was irresponsible, and Amazon’s right. This was before such public backlash against FR, and the reasoning for a default of 80% confidence was the expectation that most people using it would do silly stuff like celebrity lookalikes. That being said, it was stupid to set the default to 80%, but that’s just hindsight speaking.
My point here is that, while FR tech isn’t perfect, the public perception is highly skewed. If there was a daily news report detailing the number of correct matches across all systems, these few showing a false match would seem ridiculous. The overwhelming vast majority of news reports on FR are about failure cases. No wonder most people think the tech is fundamentally broken.
I think most systems in use today are fine in terms of accuracy. The consideration becomes “how is it being used?” That isn’t to say that improvements aren’t welcome, but in some cases it’s like trying to use the hook on the back of a hammer as a screw driver. I’m sure it can be made to work, but fundamentally it’s the wrong tool for the job.
FR in a payment system is just all wrong. It’s literally forcing the use of a tech where it shouldn’t be used. FR can be used for validation if increased security is needed, like accessing a bank account. But never as the sole means of authentication. You should still require a bank card + pin, then the system can do FR as a kind of 2FA. The trick here would be to first, use a good system, and then second, lower the threshold that borders on “fairly lenient”. That way you eliminate any false rejections while still maintaining an incredibly high level of security. In that case the chances of your bank card AND pin being stolen by someone who looks so much like you that it tricks FR is effectively impossible (but it can never be truly zero). And if that person is being targeted by a threat actor who can coordinate such things then they’d have the resources to just get around the cyber security of the bank from the comfort of anywhere in the world.
Security in every single circumstance is a trade-off with convenience. Always, and in every scenario.
FR works well with existing access control systems. Swipe your badge card, then it scans you to verify you’re the person identified by the badge.
FR also works well in surveillance, with the incredibly important addition of human-in-the-loop. For example, the system I worked on simply reported detections to a SoC (with all the general info about the detection including the live photo and the reference photo). Then the operator would have to look at the details and manually confirm or reject the detection. The system made no decisions, it simply presented the info to an authorized person.
This is the key portion that seems to be missing in all news reports about false arrests and whatnot. I’ve looked into all the FR related false arrests and from what I could determine none of those cases were handled properly. The detection results were simply taken as gospel truth and no critical thinking was applied. In some of those cases the detection photo and reference (database) photo looked nothing alike. It’s just the people operating those systems are either idiots or just don’t care. Both of those are policy issues entirely unrelated to the accuracy of the tech.
The mishandling is indeed what I’m concerned about most. I now understand far better where you’re coming from, sincere thanks for taking the time to explain. Cheers
Super interesting to read your more technical perspective. I also think facial recognition (and honestly most AI use cases) are best when used to supplement an existing system. Such as flagging a potential shoplifter to human security.
Sadly most people don’t really understand the tech they use for work. If the computer tells them something they just kind of blindly believe it. Especially in a work environment where they have been trained to do what the machine says.
My guess is that the people were trained on how to use the system at a very basic level. Troubleshooting and understanding the potential for error typically isn’t covered in 30min corporate instructional meetings. They just get a little notice saying a shoplifter is in the store and act on that without thinking.
This tech (AI detection) or purpose built facial recognition algorithms?