Back in 2024 I wrote about contraction in the identity/biometric industry.
In retrospect, that industry has been lucky.
At the time I wrote this (yes, this is a scheduled post), I knew of few if any identity/biometric companies that had “right sized” half its staff. 10% maybe, but not 50%.
I’m guilty of acronym overuse. I just wrote a post that mentioned something called “FRTE,” and I belatedly realized that many of the people who read the post…and many of the people who need to read the post…have no idea how to spell FRTE, much less WHY it’s important. So let me explain.
But before I explain FRTE, I should explain NIST. It’s the National Institute of Standards and Technology, part of the U.S. Department of Commerce, and it promotes technology standards throughout the country and throughout the world.
But I’m not going to talk about FATE today. Let’s focus on FRTE.
Why FRTE?
There are hundreds upon hundreds of algorithms out there that purport to compare a face to another face, or to compare a face to many faces, and indicate the likelihood that the compared faces belong to the same person.
And any algorithm provider can claim that its facial recognition algorithm provides 100.00% accuracy or 99.99% accuracy or whatever.
Or that it can search a trillion record database in 0.1 seconds or whatever.
Perhaps the provider even backs up this claim with published data in which the provider tested its algorithm with 1,000 searches against a 100,000 record database and the algorithm did not make a single error.
Are you impressed?
I’m not.
Anyone can score 100% on a self-test.
But what happens when you are given a test by someone else…closed book…with no answer key?
(And yes, I’m aware of the claims that these independent tests are flawed. So design a better one that more than one algorithm provider supports.)
If you’re looking to buy facial recognition technology, the second best way to evaluate the different facial recognition algorithms is to consult the NIST FRTE tests.
These tests are continuous, with new algorithms usually added monthly.
These tests are complex, measuring umpteen diffferent databases and search types. One or more of these may match your particular use case.
These tests are black box. The algorithm providers send their algorithms to NIST, and they are tested against all the other algorithms on identical setups.
Most importantly, the results of these tests are public, and you can view them yourself. The 1:1 testing is here, and the 1:N testing is here.
Oh, and the tests are listed by the algorithm provider, so if Omnigarde says they’ve been tested by NIST, you can look at the test results and find Omnigarde’s algorithm.
And if Vendor X says its algorithm tested well, but you can’t find Vendor X in the algorithm list, then you need to ask Vendor X which algorithm it’s using.
And if Vendor Y says it’s really accurate, but doesn’t state that the algorithm it uses was NIST tested…ask Vendor Y to prove its accuracy claims.
So that’s FRTE. And if your facial recognition vendor isn’t talking about FRTE…ask why.
I know that the experts say that “too much knowledge is actually bad in tech.” But based upon what I just saw from an (unnamed) identity verification company, I assert that too little knowledge is much worse.
As a biometric product marketing expert and biometric product marketing writer, I pay a lot of attention to how identity verification companies and other biometric and identity companies market themselves. Many companies know how to speak to their prospects…and many don’t.
Take a particular company, which I will not name. Here is the “marketing” from this company.
We claim high facial recognition accuracy but don’t publish our NIST FRTE results! (While the company claims to author its technology, the company name does not appear in either the NIST FRTE 1:1 or NIST FRTE 1:N results.)
We claim liveness detection (presentation attack detection) but don’t publish any confirmation letters! (Again, I could not find the company name on the confirmation letter lists from BixeLab or iBeta.)
Google Gemini.
So what is the difference between this company and the other 100+ identity verification companies…many of which explicitly state their benefits, trumpet their NIST FRTE performance, and trumpet their third-party liveness detection confirmation letters?
If you claim great accuracy and great liveness detection but can’t support it via independent third-party verification, your claim is “so what?” worthless. Prove your claims.
Now I’m sure I could help this company. Even if they have none of the certifications or confirmations I mentioned, I could at least get the company to focus on meaningful differentiation and meaningful benefits. But there’s no need to even craft a Bredemarket pitch to the company, since the only marketer on staff is an intern who is indifferent to strategy.
Google Gemini.
Because while many companies assert that all they need is a salesperson, an engineer, an African data labeler, and someone to run the generative AI for everything else…there are dozens of competitors doing the exact same thing.
But some aren’t. Some identity/biometric companies are paying attention to their long-term viability, and are creating content, proposals, and analyses that support that viability.
Take a look at your company’s marketing. Does it speak to prospects? Does it prove that you will meet your customers’ needs? Or does it sound like every other company that’s saying “We use AI. Trust us“?
And if YOUR company needs experienced help in conveying customer-focused benefits to your prospects…contact Bredemarket. I’ve delivered meaningful biometric materials to two dozen companies over the years. And yes, I have experience. Let me use it for your advantage.
Earlier this month I discussed a class action lawsuit, originated in the United States, from people who believe their privacy is being violated by the use of Kenyan data labelers to view their video output.
And the data labelers themselves are not happy, according to a 404 Media article “AI is African Intelligence.”
Before I get to the Kenyans, let’s talk about the reality of AI. No, AI output is not 100% generated by computers alone. There is often human review.
Back to Kenya and the Data Labelers Association (DLA) reports of what data labelers actually do.
“Every day, Michael Geoffrey Asia spent eight consecutive hours at his laptop in Kenya staring at porn, annotating what was happening in every frame for an AI data labeling company. When he was done with his shift, he started his second job as the human labor behind AI sex bots, sexting with real lonely people he suspected were in the United States. His boss was an algorithm that told him to flit in and out of different personas.”
I’ve previously seen reports about people in the U.S. reviewing shocking material for social media companies, but it’s a heck of a lot cheaper to outsource the work abroad.
I do offer one caution: there is a lot of data labeling work that is NOT pornographic. In the identity verification industry, data labelers review real and fake faces, real and fake documents, and the like to train AI models. Such work does not have the emotional stress that you get from watching certain videos.
Bredemarket 