If you listen closely, you can hear about all sorts of wonderful biometric identifiers. They range from the common (such as fingerprint ridges and detail) to the esoteric (my favorite was the 2013 story about Japanese car seats that captured butt prints).
Forget about fingerprints and faces and irises and DNA and gait recognition and butt prints. Tongue prints are the answer!
Benefits of tongue print biometrics
To its credit, the article does point out two benefits of using tongue prints as a biometric identifier.
Consent and privacy. Unlike fingerprints and irises (and faces) which are always exposed and can conceivably be captured without the person’s knowledge, the subject has to provide consent before a tongue image is captured. For the most part, tongues are privacy-perfect.
Liveness. The article claims that “sticking out one’s tongue is an undeniable ‘proof of life.'” Perhaps that’s an exaggeration, but it is admittedly much harder to fake a tongue than it is to fake a finger or a face.
Are tongues unique?
But the article also makes these claims.
Two main attributes are measured for a tongue print. First is the tongue shape, as the shape of the tongue is unique to everyone.
The other notable feature is the texture of the tongue. Tongues consist of a number of ridges, wrinkles, seams and marks that are unique to every individual.
There is serious doubt (if not outright denial) that everyone has a unique face (although NIST is investigating this via the FRTE Twins Demonstration).
But at least these modalities are under study. Has anyone conducted a rigorous study to prove or disprove the uniqueness of tongues? By “rigorous,” I mean a study that has evaluated millions of tongues in the same way that NIST has evaluated millions of fingerprints, faces, and irises?
I did find this 2017 tongue identification pilot study but it only included a whopping 20 participants. And the study authors (who are always seeking funding anyway) admitted that “large-scale studies are required to validate the results.”
Conclusion
So if a police officer tells you to stick out your tongue for identification purposes, think twice.
On September 30, FindBiometrics and Acuity Market Intelligence released the production version of the Biometric Digital Identity Prism Report. You can request to download it here.
But FindBiometrics and Acuity Market Intelligence didn’t invent the Big 3. The concept has been around for 40 years. And two of today’s Big 3 weren’t in the Big 3 when things started. Oh, and there weren’t always 3; sometimes there were 4, and some could argue that there were 5.
So how did we get from the Big 3 of 40 years ago to the Big 3 of today?
The Big 3 in the 1980s
Back in 1986 (eight years before I learned how to spell AFIS) the American National Standards Institute, in conjunction with the National Bureau of Standards, issued ANSI/NBS-ICST 1-1986, a data format for information interchange of fingerprints. The PDF of this long-superseded standard is available here.
When creating this standard, ANSI and the NBS worked with a number of law enforcement agencies, as well as companies in the nascent fingerprint industry. There is a whole list of companies cited at the beginning of the standard, but I’d like to name four of them.
De La Rue Printrak, Inc.
Identix, Inc.
Morpho Systems
NEC Information Systems, Inc.
While all four of these companies produced computerized fingerprinting equipment, three of them had successfully produced automated fingerprint identification systems, or AFIS. As Chapter 6 of the Fingerprint Sourcebook subsequently noted:
Morpho Systems resulted from French AFIS efforts, separate from those of the FBI. These efforts launched Morpho’s long-standing relationship with the French National Police, as well as a similar relationship (now former relationship) with Pierce County, Washington.
NEC had deployed AFIS equipment for the National Police Academy of Japan, and (after some prodding; read Chapter 6 for the story) the city of San Francisco. Eventually the state of California obtained an NEC system, which played a part in the identification of “Night Stalker” Richard Ramirez.
After the success of the San Francisco and California AFIS systems, many other jurisdictions began clamoring for AFIS of their own, and turned to these three vendors to supply them.
The Big 4 in the 1990s
But in 1990, these three firms were joined by a fourth upstart, Cogent Systems of South Pasadena, California.
While customers initially preferred the Big 3 to the upstart, Cogent Systems eventually installed a statewide system in Ohio and a border control system for the U.S. government, plus a vast number of local systems at the county and city level.
Between 1991 and 1994, the (Immigfation and Naturalization Service) conducted several studies of automated fingerprint systems, primarily in the San Diego, California, Border Patrol Sector. These studies demonstrated to the INS the feasibility of using a biometric fingerprint identification system to identify apprehended aliens on a large scale. In September 1994, Congress provided almost $30 million for the INS to deploy its fingerprint identification system. In October 1994, the INS began using the system, called IDENT, first in the San Diego Border Patrol Sector and then throughout the rest of the Southwest Border.
I was a proposal writer for Printrak (divested by De La Rue) in the 1990s, and competed against Cogent, Morpho, and NEC in AFIS procurements. By the time I moved from proposals to product management, the next redefinition of the “big” vendors occurred.
The Big 3 in 2003
There are a lot of name changes that affected AFIS participants, one of which was the 1988 name change of the National Bureau of Standards to the National Institute of Standards and Technology (NIST). As fingerprints and other biometric modalities were increasingly employed by government agencies, NIST began conducting tests of biometric systems. These tests continue to this day, as I have previously noted.
One of NIST’s first tests was the Fingerprint Vendor Technology Evaluation of 2003 (FpVTE 2003).
For those who are familiar with NIST testing, it’s no surprise that the test was thorough:
FpVTE 2003 consists of multiple tests performed with combinations of fingers (e.g., single fingers, two index fingers, four to ten fingers) and different types and qualities of operational fingerprints (e.g., flat livescan images from visa applicants, multi-finger slap livescan images from present-day booking or background check systems, or rolled and flat inked fingerprints from legacy criminal databases).
Eighteen vendors submitted their fingerprint algorithms to NIST for one or more of the various tests, including Bioscrypt, Cogent Systems, Identix, SAGEM MORPHO (SAGEM had acquired Morpho Systems), NEC, and Motorola (which had acquired Printrak). And at the conclusion of the testing, the FpVTE 2003 summary (PDF) made this statement:
Of the systems tested, NEC, SAGEM, and Cogent produced the most accurate results.
Which would have been great news if I were a product manager at NEC, SAGEM, and Cogent.
Unfortunately, I was a product manager at Motorola.
The effect of this report was…not good, and at least partially (but not fully) contributed to Motorola’s loss of its long-standing client, the Royal Canadian Mounted Police, to Cogent.
The Big 3, 4, or 5 after 2003
So what happened in the years after FpVTE was released? Opinions vary, but here are three possible explanations for what happened next.
Did the Big 3 become the Big 4 again?
Now I probably have a bit of bias in this area since I was a Motorola employee, but I maintain that Motorola overcame this temporary setback and vaulted back into the Big 4 within a couple of years. Among other things, Motorola deployed a national 1000 pixels-per-inch (PPI) system in Sweden several years before the FBI did.
Did the Big 3 remain the Big 3?
Motorola’s arch-enemies at Sagem Morpho had a different opinion, which was revealed when the state of West Virginia finally got around to deploying its own AFIS. A bit ironic, since the national FBI AFIS system IAFIS was located in West Virginia, or perhaps not.
Anyway, Motorola had a very effective sales staff, as was apparent when the state issued its Request for Proposal (RFP) and explicitly said that the state wanted a Motorola AFIS.
That didn’t stop Cogent, Identix, NEC, and Sagem Morpho from bidding on the project.
After the award, Dorothy Bullard and I requested copies of all of the proposals for evaluation. While Motorola (to no one’s surprise) won the competition, Dorothy and I believed that we shouldn’t have won. In particular, our arch-enemies at Sagem Morpho raised a compelling argument that it should be the chosen vendor.
Their argument? Here’s my summary: “Your RFP says that you want a Motorola AFIS. The states of Kansas (see page 6 of this PDF) and New Mexico (see this PDF) USED to have a Motorola AFIS…but replaced their systems with our MetaMorpho AFIS because it’s BETTER than the Motorola AFIS.”
But were Cogent, Motorola, NEC, and Sagem Morpho the only “big” players?
Did the Big 3 become the Big 5?
While the Big 3/Big 4 took a lot of the headlines, there were a number of other companies vying for attention. (I’ve talked about this before, but it’s worthwhile to review it again.)
Identix, while making some efforts in the AFIS market, concentrated on creating live scan fingerprinting machines, where it competed (sometimes in court) against companies such as Digital Biometrics and Bioscrypt.
The fingerprint companies started to compete against facial recognition companies, including Viisage and Visionics.
Oh, and there were also iris companies such as Iridian.
And there were other ways to identify people. Even before 9/11 mandated REAL ID (which we may get any year now), Polaroid was making great efforts to improve driver’s licenses to serve as a reliable form of identification.
In short, there were a bunch of small identity companies all over the place.
But in the course of a few short years, Dr. Joseph Atick (initially) and Robert LaPenta (subsequently) concentrated on acquiring and merging those companies into a single firm, L-1 Identity Solutions.
These multiple mergers resulted in former competitors Identix and Digital Biometrics, and former competitors Viisage and Visionics, becoming part of one big happy family. (A multinational big happy family when you count Bioscrypt.) Eventually this company offered fingerprint, face, iris, driver’s license, and passport solutions, something that none of the Big 3/Big 4 could claim (although Sagem Morpho had a facial recognition offering). And L-1 had federal contracts and state contracts that could match anything that the Big 3/Big 4 offered.
So while L-1 didn’t have a state AFIS contract like Cogent, Motorola, NEC, and Sagem Morpho did, you could argue that L-1 was important enough to be ranked with the big boys.
So for the sake of argument let’s assume that there was a Big 5, and L-1 Identity Solutions was part of it, along with the three big boys Motorola, NEC, and Safran (who had acquired Sagem and thus now owned Sagem Morpho), and the independent Cogent Systems. These five companies competed fiercly with each other (see West Virginia, above).
In a two-year period, everything would change.
The Big 3 after 2009
Hang on to your seats.
The Motorola RAZR was hugely popular…until it wasn’t. Eventually Motorola split into two companies and sold off others, including the “Printrak” Biometric Business Unit. By NextG50 – Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=130206087
By 2009, Safran (resulting from the merger of Sagem and Snecma) was an international powerhouse in aerospace and defense and also had identity/biometric interests. Motorola, in the meantime, was no longer enjoying the success of its RAZR phone and was looking at trimming down (prior to its eventual, um, bifurcation). In response to these dynamics, Safran announced its intent to purchase Motorola’s Biometric Business Unit in October 2008, an effort that was finalized in April 2009. The Biometric Business Unit (adopting its former name Printrak) was acquired by Sagem Morpho and became MorphoTrak. On a personal level, Dorothy Bullard moved out of Proposals and I moved into Proposals, where I got to work with my new best friends that had previously slammed Motorola for losing the Kansas and New Mexico deals. (Seriously, Cindy and Ron are great folks.)
By 2011, Safran decided that it needed additional identity capabilities, so it acquired L-1 Identity Solutions and renamed the acquisition as MorphoTrust.
If you’re keeping notes, the Big 5 have now become the Big 3: 3M, Safran, and NEC (the one constant in all of this).
While there were subsequent changes (3M sold Cogent and other pieces to Gemalto, Safran sold all of Morpho to Advent International/Oberthur to form IDEMIA, and Gemalto was acquired by Thales), the Big 3 has remained constant over the last decade.
And that’s where we are today…pending future developments.
If Alphabet or Amazon reverse their current reluctance to market their biometric offerings to governments, the entire landscape could change again.
Or perhaps a new AI-fueled competitor could emerge.
The 1 Biometric Content Marketing Expert
This was written by John Bredehoft of Bredemarket.
If you work for the Big 3 or the Little 80+ and need marketing and writing services, the biometric content marketing expert can help you. There are several ways to get in touch:
Book a meeting with me at calendly.com/bredemarket. Be sure to fill out the information form so I can best help you.
Always take advantage of your competitors’ weaknesses.
This post describes an easy way to take advantage of your competitors. If they’re not blogging, make sure your firm is blogging. And the post provides hard numbers that demonstrate why your firm should be blogging.
Which means that half of those companies don’t have a public corporate blog.
The same infographic also revealed the following:
86% of B2B companies are blogging. (Or, 14% are not.)
68% of social media marketers use blogs in their social media strategy. (Or, 32% don’t.)
45% of marketers saying blogging is the #1 most important piece of their content strategy.
Small businesses under 10 employees allocate 42% of their marketing budget to content marketing.
So obviously some firms believe blogging is important, while others don’t.
What difference does this make for your firm?
What results do blogging companies receive?
In my view, the figures above are way too low. 100% of all Fortune 500 companies, 100% of B2B companies should be blogging, and 100% of social media marketers should incorporate blogging.
Getting leads from blogging is nice, but show me the money! What about conversions?
Marketers who have prioritized blogging are 13x more likely to enjoy positive ROI.
92% of companies who blog multiple times per day have acquired a customer from their blog.
Take a look at those last two bullets related to conversion again. Blogging is correlated with positive ROI (I won’t claim causation, but anecdotally I believe it), and blogging helps firms acquire customers. So if your firm wants to make money, get blogging.
What should YOUR company do?
With numbers like this, shouldn’t all companies be blogging?
But don’t share these facts with your competitors. Keep them to yourself so that you gain a competitive advantage over them.
Now you just need to write those blog posts.
How can I help?
And if you need help with the actual writing, I, John E Bredehoft of Bredemarket, can help.
And if you’re not in the identity/biometric industry, my general content marketing expertise also applies to technology firms and general business firms.
In most cases, I can provide your blog post via my standard package, the Bredemarket 400 Short Writing Service. I offer other packages and options if you have special needs.
Authorize Bredemarket, Ontario California’s content marketing expert, to help your firm produce words that return results.
Back in 2002, this news WAS really “scary,” since it suggested that you could access a fingerprint reader-protected site with something that wasn’t a finger. Gelatin. A piece of metal. A photograph.
Except that the fingerprint reader world didn’t stand still after 2002, and the industry developed ways to detect spoofed fingers.
Basically, I had gone through great trouble to document that Bredemarket would NOT take identity work, so I had to reverse a lot of pages to say that Bredemarket WOULD take identity work.
I may have found a few additional pages after June 1, but eventually I reached the point where everything on the Bredemarket website was completely and totally updated, and I wouldn’t have to perform any other changes.
You can predict where this is going.
Who I…was
Today it occurred to me that some of the readers of the LinkedIn Bredemarket page may not know the person behind Bredemarket, so I took the opportunity to share Bredemarket’s “Who I Am” web page on the LinkedIn page.
So yes, this biometric content marketing expert/identity content marketing expert IS available for your content marketing needs. If you’re interested in receiving my help with your identity written content, contact me.
I know that I’m the guy who likes to say that it’s all semantics. After all, I’m the person who has referred to five-page long documents as “battlecards.”
But sometimes the semantics are critically important. Take the terms “factors” and “modalities.” On the surface they sound similar, but in practice there is an extremely important difference between factors of authentication and modalities of authentication. Let’s discuss.
What is a factor?
To answer the question “what is a factor,” let me steal from something I wrote back in 2021 called “The five authentication factors.”
Something You Know. Think “password.” And no, passwords aren’t dead. But the use of your mother’s maiden name as an authentication factor is hopefully decreasing.
Something You Have. I’ve spent much of the last ten years working with this factor, primarily in the form of driver’s licenses. (Yes, MorphoTrak proposed driver’s license systems. No, they eventually stopped doing so. But obviously IDEMIA North America, the former MorphoTrust, has implemented a number of driver’s license systems.) But there are other examples, such as hardware or software tokens.
Something You Are. I’ve spent…a long time with this factor, since this is the factor that includes biometrics modalities (finger, face, iris, DNA, voice, vein, etc.). It also includes behavioral biometrics, provided that they are truly behavioral and relatively static.
Something You Do. The Cybersecurity Man chose to explain this in a non-behavioral fashion, such as using swiping patterns to unlock a device. This is different from something such as gait recognition, which supposedly remains constant and is thus classified as behavioral biometrics.
Somewhere You Are. This is an emerging factor, as smartphones become more and more prevalent and locations are therefore easier to capture. Even then, however, precision isn’t always as good as we want it to be. For example, when you and a few hundred of your closest friends have illegally entered the U.S. Capitol, you can’t use geolocation alone to determine who exactly is in Speaker Pelosi’s office.
(By the way, if you search the series of tubes for reading material on authentication factors, you’ll find a lot of references to only three authentication factors, including references from some very respectable sources. Those sources are only 60% right, since they leave off the final two factors I listed above. It’s five factors of authentication, folks. Maybe.)
The one striking thing about the five factors is that while they can all be used to authenticate (and verify) identities, they are inherently different from one another. The ridges of my fingerprint bear no relation to my 16 character password, nor do they bear any relation to my driver’s license. These differences are critical, as we shall see.
What is a modality?
In identity usage, a modality refers to different variations of the same factor. This is most commonly used with the “something you are” (biometric) factor, but it doesn’t have to be.
[M]any businesses and individuals (are adopting) biometric authentication as it been established as the most secure authentication method surpassing passwords and pins. There are many modalities of biometric authentication to pick from, but which method is the best?
After looking at fingerprints, faces, voices, and irises, Aware basically answered its “best” question by concluding “it depends.” Different modalities have their own strengths and weaknesses, depending upon the use case. (If you wear thick gloves as part of your daily work, forget about fingerprints.)
ID R&D goes a step further and argues that it’s best to use multimodal biometrics, in which the two biometrics are face and voice. (By an amazing coincidence, ID R&D offers face and voice solutions.)
The three modalities in the middle—face, voice, and fingerprint—are all clearly biometric “something you are” modalities.
But the modality on the left, “Make a body movement in front of the camera,” is not a biometric modality (despite its reference to the body), but is an example of “something you do.”
Passwords, of course, are “something you know.”
In fact, each authentication factor has multiple modalities.
For example, a few of the modalities associated with “something you have” include driver’s licenses, passports, hardware tokens, and even smartphones.
Why multifactor is (usually) more robust than multimodal
Modalities within a single authentication factor are more closely related than modalities within multiple authentication factors. As I mentioned above when talking about factors, there is no relationship between my fingerprint, my password, and my driver’s license. However, there is SOME relationship between my driver’s license and my passport, since the two share some common information such as my legal name and my date of birth.
What does this mean?
If I’ve fraudulently created a fake driver’s license in your name, I already have some of the information that I need to create a fake passport in your name.
If I’ve fraudulently created a fake iris, there’s a chance that I might already have some of the information that I need to create a fake face.
However, if I’ve bought your Coinbase password on the dark web, that doesn’t necessarily mean that I was able to also buy your passport information on the dark web (although it is possible).
Can an identity content marketing expert help you navigate these issues?
As you can see, you need to be very careful when writing about modalities and factors.
You need a biometric content marketing expert who has worked with many of these modalities.
Actually, you need an identity content marketing expert who has worked with many of these factors.
So if you are with an identity company and need to write a blog post, LinkedIn article, white paper, or other piece of content that touches on multifactor and multimodal issues, why not engage with Bredemarket to help you out?
If you’re interested in receiving my help with your identity written content, contact me.
Iris recognition continues to make the news. Let’s review what iris recognition is and its benefits (and drawbacks), why Apple made the news last month, and why Worldcoin is making the news this month.
What is iris recognition?
There are a number of biometric modalities that can identify individuals by “who they are” (one of the five factors of authentication). A few examples include fingerprints, faces, voices, and DNA. All of these modalities purport to uniquely (or nearly uniquely) identify an individual.
One other way to identify individuals is via the irises in their eyes. I’m not a doctor, but presumably the Cleveland Clinic employs medical professionals who are qualified to define what the iris is.
The iris is the colored part of your eye. Muscles in your iris control your pupil — the small black opening that lets light into your eye.
But why use irises rather than, say, fingerprints and faces? The best person to answer this is John Daugman. (At this point several of you are intoning, “John Daugman.” With reason. He’s the inventor of iris recognition.)
(I)ris patterns become interesting as an alternative approach to reliable visual recognition of persons when imaging can be done at distances of less than a meter, and especially when there is a need to search very large databases without incurring any false matches despite a huge number of possibilities. Although small (11 mm) and sometimes problematic to image, the iris has the great mathematical advantage that its pattern variability among different persons is enormous.
Daugman, John, “How Iris Recognition Works.” IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 14, NO. 1, JANUARY 2004. Quoted from page 21. (PDF)
Or in non-scientific speak, one benefit of iris recognition is that you know it is accurate, even when submitting a pair of irises in a one-to-many search against a huge database. How huge? We’ll discuss later.
Brandon Mayfield and fingerprints
Remember that Daugman’s paper was released roughly two months before Brandon Mayfield was misidentified in a fingerprint comparison. (Everyone now intone “Brandon Mayfield.”)
While some of the issues associated with Mayfield’s misidentification had nothing to do with forensic science (Al Jazeera spends some time discussing bias, and Itiel Dror also looked at bias post-Mayfield), this still shows that fingerprints are remarkably similar and that it takes care to properly identify people.
Police agencies, witnesses, and faces
And of course there are recent examples of facial misidentifications (both by police agencies and witnesses), again not necessarily forensic science related, and again showing the similarity of faces from two different people.
At the root of iris recognition’s accuracy is the data-richness of the iris itself. The IrisAccess system captures over 240 degrees of freedom or unique characteristics in formulating its algorithmic template. Fingerprints, facial recognition and hand geometry have far less detailed input in template construction.
Enough about claims. What about real results? The IREX 10 test, independently administered by the U.S. National Institute of Standards and Technology, measures the identification (one-to-many) accuracy of submitted algorithms. At the time I am writing this, the ten most accurate algorithms provide false negative identification rates (FNIR) between 0.0022 ± 0.0004 and 0.0037 ± 0.0005 when two eyes are used. (Single eye accuracy is lower.) By the time you see this, the top ten algorithms may have changed, because the vendors are always improving.
IREX10 two-eye accuracy, top ten algorithms as of July 28, 2023. (Link)
While the IREX10 one-to-many tests are conducted against databases of less than a million records, it is estimated that iris one-to-many accuracy remains high even with databases of a billion people—something we will return to later in this post.
Iris drawbacks
OK, so if irises are so accurate, why aren’t we dumping our fingerprint readers and face readers and just using irises?
In short, because of the high friction in capturing irises. You can use high-resolution cameras to capture fingerprints and faces from far away, but as of now iris capture usually requires you to get very close to the capture device.
Iris image capture circa 2020 from the U.S. Federal Bureau of Investigation. (Link)
Which I guess is better than the old days when you had to put your eye right up against the capture device, but it’s still not as friendly (or intrusive) as face capture, which can be achieved as you’re walking down a passageway in an airport or sports stadium.
Irises and Apple Vision Pro
So how are irises being used today? You may or may not have hard last month’s hoopla about the Apple Vision Pro, which uses irises for one-to-one authetication.
I’m not going to spend a ton of time delving into this, because I just discussed Apple Vision Pro in June. In fact, I’m just going to quote from what I already said.
In short, as you wear the headset (which by definition is right on your head, not far away), the headset captures your iris images and uses them to authenticate you.
It’s a one-to-one comparison, not the one-to-many comparison that I discussed earlier in this post, but it is used to uniquely identify an individual.
But iris recognition doesn’t have to be used for identification.
Irises and Worldcoin
“But wait a minute, John,” you’re saying. “If you’re not using irises to determine if a person is who they say they are, then why would anyone use irises?”
Over the past several years, I’ve analyzed a variety of identity firms. Earlier this year I took a look at Worldcoin….Worldcoin’s World ID emphasizes privacy so much that it does not conclusively prove a person’s identity (it only proves a person’s uniqueness)…
That’s the only thing that I’ve said about Worldcoin, at least publicly. (I looked at Worldcoin privately earlier in 2023, but that report is not publicly accessible and even I don’t have it any more.)
The Worldcoin Foundation today announced that Worldcoin, a project co-founded by Sam Altman, Alex Blania and Max Novendstern, is now live and in a production-grade state.
The launch includes the release of the World ID SDK and plans to scale Orb operations to 35+ cities across 20+ countries around the world. In tandem, the Foundation’s subsidiary, World Assets Ltd., minted and released the Worldcoin token (WLD) to the millions of eligible people who participated in the beta; WLD is now transactable on the blockchain….
“In the age of AI, the need for proof of personhood is no longer a topic of serious debate; instead, the critical question is whether or not the proof of personhood solutions we have can be privacy-first, decentralized and maximally inclusive,” said Worldcoin co-founder and Tools for Humanity CEO Alex Blania. “Through its unique technology, Worldcoin aims to provide anyone in the world, regardless of background, geography or income, access to the growing digital and global economy in a privacy preserving and decentralized way.”
Worldcoin does NOT positively identify people…but it can still pay you
A very important note: Worldcoin’s purpose is not to determine identity (that a person is who they say they are). Worldcoin’s purpose is to determine uniqueness: namely, that a person (whoever they are) is unique among all the billions of people in the world. Once uniqueness is determined, the person can get money money money with an assurance that the same person won’t get money twice.
Iris biometrics outperform other biometric modalities and already achieved false match rates beyond 1.2× 10−141.2×10−14 (one false match in one trillion[9]) two decades ago[10]—even without recent advancements in AI. This is several orders of magnitude more accurate than the current state of the art in face recognition.
When you have tens of thousands of people dying, then the only conscionable response is to ban automobiles altogether. Any other action or inaction is completely irresponsible.
After all, you can ask the experts who want us to ban biometrics because it can be spoofed and is racist, so therefore we shouldn’t use biometrics at all.
I disagree with the calls to ban biometrics, and I’ll go through three “biometrics are bad” examples and say why banning biometrics is NOT justified.
Even some identity professionals may not know about the old “gummy fingers” story from 20+ years ago.
And yes, I know that I’ve talked about Gender Shades ad nauseum, but it bears repeating again.
And voice deepfakes are always a good topic to discuss in our AI-obsessed world.
But the iris security was breached by a “dummy eye” just a month later, in the same way that gummy fingers and face masks have defeated other biometric technologies.
Back in 2002, this news WAS really “scary,” since it suggested that you could access a fingerprint reader-protected site with something that wasn’t a finger. Gelatin. A piece of metal. A photograph.
TECH5 participated in the 2023 LivDet Non-contact Fingerprint competition to evaluate its latest NN-based fingerprint liveness detection algorithm and has achieved first and second ranks in the “Systems” category for both single- and four-fingerprint liveness detection algorithms respectively. Both submissions achieved the lowest error rates on bonafide (live) fingerprints. TECH5 achieved 100% accuracy in detecting complex spoof types such as Ecoflex, Playdoh, wood glue, and latex with its groundbreaking Neural Network model that is only 1.5MB in size, setting a new industry benchmark for both accuracy and efficiency.
TECH5 excelled in detecting fake fingers for “non-contact” reading where the fingers don’t even touch a surface such as an optical surface. That’s appreciably harder than detecting fake fingers that touch contact devices.
I should note that LivDet is an independent assessment. As I’ve said before, independent technology assessments provide some guidance on the accuracy and performance of technologies.
So gummy fingers and future threats can be addressed as they arrive.
Let’s stop right there for a moment and address two items before we continue. Trust me; it’s important.
This study evaluated only three algorithms: one from IBM, one from Microsoft, and one from Face++. It did not evaluate the hundreds of other facial recognition algorithms that existed in 2018 when the study was released.
The study focused on gender classification and race classification. Back in those primitive innocent days of 2018, the world assumed that you could look at a person and tell whether the person was male or female, or tell the race of a person. (The phrase “self-identity” had not yet become popular, despite the Rachel Dolezal episode which happened before the Gender Shades study). Most importantly, the study did not address identification of individuals at all.
However, the findings did find something:
While the companies appear to have relatively high accuracy overall, there are notable differences in the error rates between different groups. Let’s explore.
All companies perform better on males than females with an 8.1% – 20.6% difference in error rates.
All companies perform better on lighter subjects as a whole than on darker subjects as a whole with an 11.8% – 19.2% difference in error rates.
When we analyze the results by intersectional subgroups – darker males, darker females, lighter males, lighter females – we see that all companies perform worst on darker females.
What does this mean? It means that if you are using one of these three algorithms solely for the purpose of determining a person’s gender and race, some results are more accurate than others.
And all the stories about people such as Robert Williams being wrongfully arrested based upon faulty facial recognition results have nothing to do with Gender Shades. I’ll address this briefly (for once):
In the United States, facial recognition identification results should only be used by the police as an investigative lead, and no one should be arrested solely on the basis of facial recognition. (The city of Detroit stated that Williams’ arrest resulted from “sloppy” detective work.)
If you are using facial recognition for criminal investigations, your people had better have forensic face training. (Then they would know, as Detroit investigators apparently didn’t know, that the quality of surveillance footage is important.)
If you’re going to ban computerized facial recognition (even when only used as an investigative lead, and even when only used by properly trained individuals), consider the alternative of human witness identification. Or witness misidentification. Roeling Adams, Reggie Cole, Jason Kindle, Adam Riojas, Timothy Atkins, Uriah Courtney, Jason Rivera, Vondell Lewis, Guy Miles, Luis Vargas, and Rafael Madrigal can tell you how inaccurate (and racist) human facial recognition can be. See my LinkedIn article “Don’t ban facial recognition.”
Obviously, facial recognition has been the subject of independent assessments, including continuous bias testing by the National Institute of Standards and Technology as part of its Face Recognition Vendor Test (FRVT), specifically within the 1:1 verification testing. And NIST has measured the identification bias of hundreds of algorithms, not just three.
Richard Nixon never spoke those words in public, although it’s possible that he may have rehearsed William Safire’s speech, composed in case Apollo 11 had not resulted in one giant leap for mankind. As noted in the video, Nixon’s voice and appearance were spoofed using artificial intelligence to create a “deepfake.”
In early 2020, a branch manager of a Japanese company in Hong Kong received a call from a man whose voice he recognized—the director of his parent business. The director had good news: the company was about to make an acquisition, so he needed to authorize some transfers to the tune of $35 million. A lawyer named Martin Zelner had been hired to coordinate the procedures and the branch manager could see in his inbox emails from the director and Zelner, confirming what money needed to move where. The manager, believing everything appeared legitimate, began making the transfers.
What he didn’t know was that he’d been duped as part of an elaborate swindle, one in which fraudsters had used “deep voice” technology to clone the director’s speech…
Now I’ll grant that this is an example of human voice verification, which can be as inaccurate as the previously referenced human witness misidentification. But are computerized systems any better, and can they detect spoofed voices?
IDVoice Verified combines ID R&D’s core voice verification biometric engine, IDVoice, with our passive voice liveness detection, IDLive Voice, to create a high-performance solution for strong authentication, fraud prevention, and anti-spoofing verification.
Anti-spoofing verification technology is a critical component in voice biometric authentication for fraud prevention services. Before determining a match, IDVoice Verified ensures that the voice presented is not a recording.
This is only the beginning of the war against voice spoofing. Other companies will pioneer new advances that will tell the real voices from the fake ones.
As for independent testing:
ID R&D has participated in multiple ASVspoof tests, and performed well in them.
Companies often have a lot of things they want to do, but don’t have the people to do them. It takes a long time to hire someone, and it even takes time to find a consultant that knows your industry and can do the work.
This affects identity/biometric companies just like it affects other companies. When an identity/biometric company needs a specific type of expertise and needs it NOW, it’s often hard to find the person they need.
If your company needs a biometric content marketing expert (or an identity content marketing expert) NOW, you’ve come to the right place—Bredemarket. Bredemarket has no identity learning curve, no content learning curve, and offers proven results.
Identity/biometric consulting in the 1990s
I remember when I first started working as an identity/biometric consultant, long before Bredemarket was a thing.
OK, not quite THAT long ago. I started working in biometrics in the 1990s—NOT the 1940s.
In 1994, the proposals department at Printrak International needed additional writers due to the manager’s maternity leave, and she was so valuable that Printrak needed to bring in TWO consultants to take her place.
At least initially, the other consultant and I couldn’t fill the manager’s shoes.
Both of us could spell “RAID.” Not the bug spray, but the storage mechanism that stored all those “huge” fingerprint images.
But on that first night that I was cranking out proposal letters for something called a “Latent Station 2000,” I didn’t really know WHAT I was writing about.
As time went on, the other consultant and I learned much more—so much that the company brought both of us on as full-time employees.
After we were hired full-time, we spent a combined 45+ years at Printrak and its corporate successors in proposals, marketing, and product management positions, contributing to industry knowledge.
But neither of us knew biometrics before we started consuting at Printrak.
And I had never written a proposal before I started consulting at Printrak. (I had written an RFP. Sort of.)
But frankly, there weren’t a lot of identity/biometric consultants out in the field in the 1990s. There were the 20th century equivalents of Applied Forensic Services LLC, but at the time I don’t think there were any 20th century equivalents of Tandem Technical Writing LLC.
Unlike the 1990s, identity/biometric firms that need consulting help have many options. In addition to Applied Forensic Services and Tandem Technical Writing you have…me.
Mike and Laurel can tell you what they can do, and I heartily endorse both of them.
Let me share with you why I call myself a biometric content marketing expert who can help your identity/biometric company get marketing content out now:
No identity learning curve
No content learning curve
Proven results
No identity learning curve
I have worked with finger, face, iris, DNA, and other biometrics, as well as government-issued identity documents and geolocation. If you are interested, you can read my Bredemarket blog posts that mention the following topics:
Because I’ve produced both external and internal content on identity/biometric topics, I offer the experience to produce your content in a number of formats.
External content: account-based marketing content, articles, blog posts (I am the identity/biometric blog expert), case studies, data sheets, partner comarketing content, presentations, proposals, sales literature sheets, scientific book chapters, smartphone application content (events), social media posts, web page content, and white papers.
You see, my fingerprint experience was primarily rooted in the traditional 14 (yes, 14) fingerprint impression block livescan capture technology used by law enforcement agencies to submit full sets of tenprints to the U.S. Federal Bureau of Investigation (FBI), and state and local agencies that submit to the FBI.
I’d be willing to bet that the vast majority of you have ten fingers.
So why do tenprint livescan devices capture 14 fingerprint impression blocks?
Why 14 fingerprint impression blocks are as good as 20 fingers
It’s important to understand that tenprint livescan devices, which only began to emerge in the 1980s, were originally designed as an electronic way to duplicate the traditional inking process in which ink was placed on arrestees’ fingers, and the ink was transferred to a tenprint fingerprint card.
The criminal fingeprint card (and, with some changes, the applicant fingerprint card) looks something like this:
If you look at the lower half of the front of a fingerprint card, you will see 14 fingerprint impression blocks arranged in 3 rows.
The first row is where you place five “rolled” (nail to nail) fingerprints taken from the right hand, starting with the right thumb and ending with the right little finger.
The second row is where you place five rolled fingerprints from the left hand, again starting with the thumb and ending with the little finger.
So now you’ve captured ten fingerprints. But you’re not done. You still have to fill four more impression blocks. Here’s how:
Identification flat impressions are taken simultaneously without rolling. These are referred to as plain, slap, or flat impressions. The individual’s right and left four fingers should be captured first, followed by the two thumbs (4-4-2 method).
To clarify, on the third row, for the large box in the lower left corner of the card, you “slap” all four fingers of the left hand down at the same time. Then you skip over the the large box on the lower right corner of the card and slap all four fingers of the right hand down at the same time. Finally you slap the two thumbs down at the same time, capturing the left thumb in the small middle left box, and the right thumb in the small middle right box.
Well, at least that’s how you do it on a traditional inked card. On a tenprint livescan device, you roll and slap your fingers on the large platen, without worrying (that much) about staying within the lines.
Why 14 fingerprint impression blocks are better than 20 fingers
So by the time you’re done, you’ve filled 14 fingerprint impression blocks by 13 distinct actions (the two slap thumbs are captured simultaneously), and you’ve effectively captured 20 fingerprints.
Why?
Quality control.
Because since every finger should theoretically be captured twice, the slaps can be compared against the rolls to ensure that the fingerprints were captured in the correct order.
Locations of finger 2 (green) and finger 3 (blue) for rolled and slap prints.
If you capture the rolled and slap prints in the correct order, then the right index finger (finger 2) should appear in the green area on the first row as a rolled print, and in the green area on the third row as a slap print. Similarly, the middle finger (finger 3) should appear in the blue areas.
If the green rolled print is NOT the same as the green slap print, or if the blue rolled print is NOT the same as the blue slap print, then you captured the fingerprints in the wrong order.
In the old pre-livescan days of inking, a trained tenprint fingerprint examiner (or someone who pretended to be one) had to look at the prints to ensure that the fingers were captured properly. Now the roll to slap comparisons are all done in software, either at the tenprint livescan device itself, or at the automated fingerprint identification system (AFIS) or the automated biometric identification system (ABIS) that receives the prints.
In the 4-4-2 method, groups of prints are captured together, rather than individually. While it is possible to completely mess things up by capturing the left slaps when you are supposed to capture the right slaps, or by twisting your hands in a bizarre manner to capture the thumbs in reverse order, 4-4-2 gives you a reasonable assurance that the slap prints are captured in the correct order, ensuring a proper roll-to-slap comparison.
Well, unless the fingerprints are captured in an unattended fashion, or the police officer capturing the fingerprints is crooked.
But today’s ABIS systems are powerful enough to compare all ten submitted fingers against all ten fingers of every record in an ABIS database, so even if the submitted fingerprints falsely record finger 2 as finger 3, the ABIS will still find the matching print anyway.
Bredemarket 