If you ask any one of us in the identity verification industry, we’ll tell you how identity verification proves that you know who is accessing your service.
During the identity verification/onboarding step, one common technique is to capture the live face of the person who is being onboarded, then compare that to the face captured from the person’s government identity document. As long as you have assurance that (a) the face is live and not a photo, and (b) the identity document has not been tampered, you positively know who you are onboarding.
The authentication step usually captures a live face and compares it to the face that was captured during onboarding, thus positively showing that the right person is accessing the previously onboarded account.
Sound like the perfect solution, especially in industries that rely on age verification to ensure that people are old enough to access the service.
Therefore, if you are employing robust identity verification and authentication that includes age verification, this should never happen.
Eduardo Montanari, who manages delivery logistics at a burger shop north of São Paulo, has noticed a pattern: Every time an order pickup is assigned to a female driver, there’s a good chance the worker is a minor.
On YouTube, a tutorial — one of many — explains “how to deliver as a minor.” It has over 31,000 views. “You have to create an account in the name of a person who’s the right age. I created mine in my mom’s name,” says a boy, who identifies himself as a minor in the video.
Once a cooperative parent or older sibling agrees to help, the account is created in the older person’s name, the older person’s face and identity document is used to create the account, and everything is valid.
Outsmarting authentication
Yes, but what about authentication?
That’s why it’s helpful to use a family member, or someone who lives in the minor’s home.
Let’s say little Maria is at home, during her homework, when her gig economy app rings with a delivery request. Now Maria was smart enough to have her older sister Irene or her mama Cecile perform the onboarding with the delivery app. If she’s at home, she can go to Irene or Cecile, have them perform the authentication, and then she’s off on her bike to make money.
(Alternatively, if the app does not support liveness detection, Maria can just hold a picture of Irene or Cecile up to the camera and authenticate.)
The onboarding process was completed by the account holder.
The authentication was completed by the account holder.
But the account holder isn’t the one that’s actually using the service. Once authentication is complete, anyone can access the service.
So how do you stop underage gig economy use?
According to Rest of World, one possible solution is to tattle on underage delivery people. If you see something, say something.
But what’s the incentive for a restaurant owner or delivery recipient to report that their deliveries are being performed by a kid?
“The feeling we have is that, at least this poor boy is working. I know this is horrible, but here in Brazil we end up seeing it as an opportunity … It’s ridiculous,” (psychologist Regiane Couto) said.
A much better solution is to replace one-time authetication with continuous authentication, or at least be smarter in authentication. For example, a gig delivery worker could be required to authenticate at multiple points in the process:
When the worker receives the delivery request.
When the worker arrives at the restaurant.
When the worker makes the delivery.
It’s too difficult to drag big sister Irene or mama Cecile to ALL of these points.
As an added bonus, these authetications provide timestamps of critical points in the delivery process, which the delivery company and/or restaurant can use for their analytics.
Problem solved.
Except that little Maria doesn’t have any excuse and has to complete her homework.
At the highest level, debates regarding government and enterprise use of biometric technology boil down to a debate about whether to keep people safe, or whether to preserve individual privacy.
In the state of Montana, school safety is winning over school privacy—for now.
The state Legislature earlier this year passed a law barring state and local governments from continuous use of facial recognition technology, typically in the form of cameras capable of reading and collecting a person’s biometric data, like the identifiable features of their face and body. A bipartisan group of legislators went toe-to-toe with software companies and law enforcement in getting Senate Bill 397 over the finish line, contending public safety concerns raised by the technology’s supporters don’t overcome individual privacy rights.
School districts, however, were specifically carved out of the definition of state and local governments to which the facial recognition technology law applies.
At a minimum Montana school districts seek to abide by two existing Federal laws when installating facial recognition and video surveillance systems.
Without many state-level privacy protection laws in place, school policies typically lean on the Children’s Online Privacy Protection Act (COPPA), a federal law requiring parental consent in order for websites to collect data on their children, or the Family Educational Rights and Privacy Act (FERPA), which protects the privacy of student education records.
If a vendor doesn’t agree to abide by these laws, then the Montana School Board Association recommends that the school district not do business with the vendor.
The Family Educational Rights and Privacy Act was passed by the US federal government to protect the privacy of students’ educational records. This law requires public schools and school districts to give families control over any personally identifiable information about the student.
(The Sun River Valley School District’s) use of the technology is more focused on keeping people who shouldn’t be on school property away, he said, such as a parent who lost custody of their child.
(Simms) High School Principal Luke McKinley said it’s been more frequent to use the facial recognition technology during extra-curricular activities, when football fans get too rowdy for a high school sports event.
Technology (in this case from Verkada) helps the Sun River School District, especially in its rural setting. Back in 2022, it took law enforcement an estimated 45 minutes to respond to school incidents. The hope is that the technology could identify those who engaged in illegal activity, or at least deter it.
What about other school districts?
When I created my educational identity page, I included the four key words “When permitted by law.” While Montana school districts are currently permitted to use facial recognition and video surveillance, other school districts need to check their local laws before implementing such a system, and also need to ensure that they comply with federal laws such as COPPA and FERPA.
I may be, um, biased in my view, but as long as the school district (or law enforcement agency, or apartment building owner, or whoever) complies with all applicable laws, and implements the technology with a primary purpose of protecting people rather than spying on them, facial recognition is a far superior tool to protect people than manual recognition methods that rely on all-too-fallible human beings.
At least in the United States, the mobile driver’s license world is fragmented.
Because driver’s license issuance in the U.S. is a state and not a federal responsibility, each state has to develop its own mobile driver’s license implementation. Subject to federal and international standards, of course.
To date there have been two parties helping the states with this:
mDL vendors such as Envoc and IDEMIA, who work with the states to create mDLs.
Operating system vendors such as Apple and Google, who work with the states to incorporate mDLs in smartphone wallets.
But because the Android ecosystem is more fragmented than the iOS ecosystem, we now have a third party that is involved in mDLs. In addition to mDL vendors and operating system vendors, we also have really large smartphone providers.
Samsung Electronics America today announced it is bringing mobile driver’s licenses and state IDs to Samsung Wallet. Arizona and Iowa will be the first states to offer a mobile version of its driver’s license to their residents. The update expands the Samsung Wallet experience by adding a convenient and secure way to use state-issued IDs and driver’s licenses
In this particular case Samsung is working with IDEMIA (the mDL provider for Arizona and Iowa), but Samsung announced that it is working with other states and with the Transportation Security Administration (TSA).
On a personal note, I’m still working on validating my driver’s license for California’s pilot mDL program. It probably didn’t help that I renewed my physical driver’s license right in the middle of the mDL validation process.
I just made an exception to my “no bots on the first draft” approach. (The Product Marketing Alliance made me do it.)
The setting
I recently attended the Product Marketing Alliance’s “Masters of Product Marketing” webinar, and the Wednesday morning (my time) workshop was led by Elliott Rayner of ARION.
The title? “Workshop – AI-powered storytelling: the definitive guide.”
Red flags. The “temperamental writer” side of me always reacts negatively at the suggestion that a person turn the writing process over to a bot.
Here is the generic prompt that Rayner shared with the workshop participants (note that this should be Christopher Booker, not Charles Brooker; generative AI figured things out anyway):
“Using Charles Brooker’s plot framework “Overcoming the monster” can you write a product campaign narrative for the brand BLANK who are on a mission to BLANK. Please place focus on the customer overcoming the pain paint of BLANK within the market segment of BLANK”
From the Product Marketing Alliance workshop
My prompt
So I took the prompt above and modified it for Bredemarket.
“Using Charles Brooker’s plot framework “Overcoming the monster” can you write a product campaign narrative for the brand Bredemarket who are on a mission to equip customers with superior marketing content. Please place focus on the customer overcoming the pain paint of addressing customer needs within the market segment of financial services”
While Elliott used ChatGPT for his generative AI engine, I used Bard. The next section contains Bard’s response to the prompt above.
Bredemarket: Overcoming the Monster of Marketing
Plot:
Once upon a time, in the competitive and ever-changing world of financial services, there lived a company called Bredemarket. Bredemarket’s mission was to equip customers with superior marketing content, but they faced a daunting challenge: how to address the pain point of addressing customer needs within a complex and ever-evolving market segment?
Bredemarket knew that the key to overcoming this monster was to create marketing content that was both informative and engaging. They needed to create content that would educate customers about their products and services, while also inspiring them to take action.
To do this, Bredemarket assembled a team of experienced and talented marketers who were experts in the financial services industry. Together, they developed a comprehensive content strategy that included a variety of formats, such as blog posts, infographics, e-books, and videos.
Bredemarket’s content was also highly targeted. They took the time to understand the needs of their target audience and created content that was specifically relevant to them. This approach helped Bredemarket to build trust and credibility with their customers.
Over time, Bredemarket’s content marketing efforts began to pay off. They saw an increase in website traffic, leads, and sales. Their customers were also more engaged and satisfied than ever before.
Conclusion:
Bredemarket’s story is a testament to the power of content marketing. By creating informative and engaging content that addresses customer needs, Bredemarket was able to overcome the monster of marketing and achieve success.
How Bredemarket can help financial services companies overcome the monster of marketing:
Bredemarket understands the unique challenges that financial services companies face when it comes to marketing. That’s why they offer a variety of services that can help companies overcome these challenges and achieve their marketing goals.
Bredemarket’s services include:
Content strategy development
Content creation
Content distribution
Content marketing analytics
Bredemarket also offers a variety of industry-specific content packages that are specifically designed for financial services companies. These packages include content that covers a wide range of topics, such as:
Financial planning
Retirement planning
Investing
Insurance
Mortgages
If you’re a financial services company that’s struggling to address customer needs with your marketing content, Bredemarket can help. With their expertise and experience in the financial services industry, they can help you create content that is both informative and engaging, and that will help you achieve your marketing goals.
My observations on the result above
So here’s what I think of Bard’s work.
If I took time to improve the prompt, I probably could have received a better result. I’ve previously said that you may have to go through five iterations of a generative AI prompt to get satisfactory results, which led to my conclusion that AI yields results in five minutes (and that humans will therefore be pressured to yield quick results also). So if I took the time to tweak the prompt, rather than just going with the first result I received, I’d get a better story. For example, rather than using the term “financial services,” perhaps I should have used “financial identity verification” to hone in on my interest in financial identity.
Hallucination is rampant in the text. When generative AI doesn’t know something, it loudly asserts what it doesn’t know. Bard obviously doesn’t know a lot about Bredemarket, but it loudly proclaimed that I provide “retirement planning.” (If I knew anything about retirement planning, I’d retire by now.) And the idea of the “team of experienced and talented marketers” is kinda sorta inaccurate. You just have me.
The tone of voice is all wrong. One reason that I would never use this result for real is because it is not in Bredemarket’s conversational tone of voice. And it would be unusual for me to tell an odyssey. I’ll leave that to John Sculley. To get Bard to write like me, perhaps I can design a prompt that includes the words “mention wildebeests a lot in the response.”
Despite these drawbacks, the exercise was helpful as a brainstorming tool. It provides a framework that would allow me to write a REAL post about how Bredemarket can help financial firms (and vendors to such firms) communicate a customer-focused message about financial identity.
So in the end, it was a worthwhile exercise.
Postscript
This isn’t the first time that I’ve written about the song “The Girl and the Robot.” Roughly a decade ago, I wrote a piece for the online MungBeing Magazine entitled “Robots Dot Txt.” This wasn’t about the official video for the song, but another video documenting a “live” performance of the song.
So in the Senkveld performance, Robyn and Röyksopp (and Davide Rossi and Anneli Drecker, not present on stage but present nevertheless) make me happy by becoming flesh-and-blood robots themselves, capably performing a variety of often complex human tasks that were programmed in a recording studio several months previously.
It’s the end of an era for a once-critical pandemic document: The ubiquitous white COVID-19 vaccination cards are being phased out.
Now that COVID-19 vaccines are not being distributed by the federal government, the U.S. Centers for Disease Control and Prevention has stopped printing new cards.
This doesn’t affect the validity of current cards. It just means that if you get a COVID vaccine, or any future vaccine, and you need to prove you obtained it, you will have to contact the medical facility who administered it.
Or, in selected states (because in the U.S. health is generally a state and not a federal responsibility), you can access the state’s digital health information. For example, the state of Washington offers MyIRmobile, as do the states of Arizona, Louisiana, Maryland, Mississippi, North Dakota, and West Virginia.
Sign up for MyIR Mobile by going to myirmobile.com and follow the registration instructions. Your registration information will be used to match your records with the state immunization registry. You will be sent a verification code on your phone to finalize the process. Once registration is complete, you’ll be able to view your immunization records, Certificate of Immunization Status (CIS) and access your COVID-19 vaccination certificate.
And one of those records was so unmemorable that it was memorable.
The album, recorded in the early to mid 1960s, trumpeted the fact that the group that recorded the album was extremely versatile. You see, the record not only included surf songs, but also included car songs!
The only problem? The album was NOT by the Beach Boys.
Instead, the album was from some otherwise unknown band that was trying to achieve success by doing what the competition did. (In this case, the Beach Boys.)
I can’t remember the name of the band, and I bet no one else can either.
“Me too” in computing and lawn care
Sadly, this tactic of Xeroxing (or Mitaing) the competition is not confined to popular music. Have you noticed that so many recipes for marketing success involve copying what your competitors do?
Semrush: “Analyze your competitors’ keywords that you are not ranking for to discover gaps in your SEO strategy.”
iSpionage: “If you can emulate your competitors but do things slightly better you have a good chance of being successful.”
Someone who shall remain nameless: “Look at this piece of collateral that one of our competitors did. We should do something just like that.”
And of course the tactic of slavishly copying competitors has been proven to work. For example, remember when Apple Computer adopted the slogan “Think The Same” as the company dressed in blue, ensured all its computers could run MS-DOS, and otherwise imitated everything that IBM did?
“But John,” you are saying. “That’s unfair. Not everyone can be Apple.”
My point exactly. Everyone can’t be Apple because they’re so busy trying to imitate someone else—either a competitor or some other really popular company.
Personally, I’m waiting for some company to claim to be “the Bredemarket of satellite television. (Which would simply mean that the company would have a lot of shows about wildebeests.) But I’ll probably have to wait a while for some company to be the Bredemarket of anything.
(An aside: while talking with a friend, I compared the British phrase “eating your pudding” to the American phrase “eating your own dog food,” although I noted that “I like to say ‘eating your own wildebeest food‘ just to stand out.” Let’s see ChatGPT do THAT.)
“Me too” in identity verification
Now I’ll tread into more dangerous territory.
Here’s an example from the identity/biometric world. Since I self-identity (heh) as the identity content marketing expert, I’m supremely qualified to cite this example.
I spent a year embedded in the identity verification industry, and got to see the messaging from my own company and by the competition.
After a while, I realized that most of the firms in the industry were saying the same thing. Here are a few examples. See if you can spot the one word that EVERY company is using:
(Company I) “Reimagine trust.”
(Company J) “To protect against fraud and financial crime, businesses online need to know and trust that their customers are who they claim to be — and that these customers continue to be trustworthy.”
(Company M) “Trust is the core of any successful business relationship. As the digital revolution continues to push businesses and financial industries towards digital-first services, gaining digital trust with consumers will be of utmost importance for survival.”
(Company O) “Create trust at onboarding and beyond with a complete, AI-powered digital identity solution built to help you know your customers online.”
(Company P) “Trust that users are who they say they are, and gain their trust by humanizing the identity experience.”
(Company V) “Stop fraud. Build trust. Identity verification made simple.”
Yes, these companies, and many others, prominently feature the t-word in their messaging.
Now perhaps some of you would argue that trust is essential to identity verification in the same way that water is essential to an ocean, and that therefore EVERYBODY HAS to use the t-word in their communications.
After all, if I was going to create content for this prospect, we had to ensure that the content stood out from their competitors.
Without revealing confidential information, I can say that I asked the firm why they were better than every other firm out there, and why all the other firms sucked. And the firm provided me with a compelling answer to that question. I can’t reveal that answer, but you can probably guess that the word “trust” was not involved.
A final thought
So let me ask you:
Why is YOUR firm better than every other firm out there, and why do all or YOUR competitors suck?
Your firm’s survival may depend upon communicating that answer.
While I don’t use all the marketing tools at my disposal, I am certainly curious about them. After all, such tools provide marketers with powerful insights on their prospects and customers.
I became especially curious about one marketing tool when re-examining a phrase I use often.
I use the phrase “biometric content marketing expert” in a non-traditional way. When I use it, I am attempting to say that I am a content marketing expert on the use of biometrics for identification. In other words, I can create multiple types of content that discusses fingerprint identification, facial recognition, and similar technologies.
But if you speak to a normal person, they will assume that a “biometric content marketing expert” is someone who uses biometrics (the broader term, not the narrower term) to support content marketing. This is something very different—something that is generally known as “facial coding,” a technique that purports to provide information to marketers.
We all know that our face conveys emotions through facial expressions; facial coding is the process of measuring those human emotions. With the help of computer vision, powered by AI and machine learning, emotions can be detected via webcam or mobile cam. The tech tracks every muscle movement on the face or all-action units (AU) based on the FACS (facial action coding system).
The differences between facial coding and facial recognition
Unlike the topics in which I usually dwell, facial coding:
Does not identify individuals. Many people can share the same emotions, so detection of a particular emotion does not serve as individualization.
Does not provide permanent information. In the course of watching a movie or even a short advertisement, viewers often exhibit a wide range of emotions. Just because you exhibit a particular emotion at the beginning of an ad doesn’t mean you’ll exhibit the same emotion at the conclusion.
As Rathi describes the practice, it preserves privacy by allowing people to opt-in, and to record the emotions anonymously.
So, the user’s permission is required to access their camera and all this data is captured with consent. And no video is shared. Only the emotion data of the users are captured through their facial expressions and shared in real-time. The emotions on a person’s face are captured as binary units (0 and 1). Hence no PII (Personally Identifiable Information) related to race, ethnicity, gender, or age is captured at any point in time.
But what if another firm chooses to gather more data, thus reducing the anonymity of the data collected? “I don’t only want to know how people react to the content. I want to know how black women in their 30s react to the content.”
And what if another firm (or a government agency, such as the Transportation Security Administration) chooses to gather the data without explicit consent, or with consent buried deep in the terms of service? In that case, people may not even realize that their facial expressions are being watched.
Examining facial expressions is not the only way to decipher what is happening in a person’s mind as they view content. But it’s powerful.
Well, maybe.
Does everyone exhibit the same facial coding?
The underlying assumption behind emotion recognition is that you can identify emotions at a universal level. If content makes me happy, or if it makes a person halfway around the world happy, we will exhibit the same measurable facial characteristics.
Research has not revealed a consistent, physical fingerprint for even a single emotion. When scientists attach electrodes to a person’s face and measure muscle movement during an emotion, they find tremendous variety, not uniformity. They find the same variety with the body and brain. You can experience anger with or without a spike in blood pressure. You can experience fear with or without a change in the amygdala, the brain region tagged as the home of fear.
When scientists set aside the classical view and just look at the data, a radically different explanation for emotion comes to light. We find that emotions are not universal but vary from culture to culture. They are not triggered; you create them. They emerge as a combination of the physical properties of your body, a flexible brain that wires itself to whatever environment it develops in, and your culture and upbringing.
If Barrett is correct, then how reliable is facial coding, even within a particular region? After all, even Southern California does not have a single universal culture, but is made up of many cultures in which people react in many different ways. And if we preserve privacy by NOT collecting this cultural information, then we may not fully understand the codings that the cameras record.
Back to the familiar “biometric” world
And with that, I will retreat from the broader definition of biometrics to the narrower and more familiar one, as described here.
The term “Biometrics” has also been used to refer to the field of technology devoted to the identification of individuals using biological traits, such as those based on retinal or iris scanning, fingerprints, or face recognition. Neither the journal “Biometrics” nor the International Biometric Society is engaged in research, marketing, or reporting related to this technology.
For those who don’t know, the Prism presents an organized view of all of the digital identity companies—or at least the ones that FindBiometrics and Acuity Market Intelligence knew about. In the last few days, they were literally beggin’ to give companies a last chance for inclusion.
On Monday, I began to see a trickle of companies that talked about their place on the Prism, including iProov and Trustmatic.
I tend to view presentation attack detection (PAD) through the lens of iBeta or occasionally of BixeLab. But I need to remind myself that these are not the only entities examining PAD.
A recent paper authored by Koushik Srivatsan, Muzammal Naseer, and Karthik Nandakumar of the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) addresses PAD from a research perspective. I honestly don’t understand the research, but perhaps you do.
Flip spoofing his natural appearance by portraying Geraldine. Some were unable to detect the attack. By NBC Television. – eBay itemphoto frontphoto back, Public Domain, https://commons.wikimedia.org/w/index.php?curid=16476809
Face anti-spoofing (FAS) or presentation attack detection is an essential component of face recognition systems deployed in security-critical applications. Existing FAS methods have poor generalizability to unseen spoof types, camera sensors, and environmental conditions. Recently, vision transformer (ViT) models have been shown to be effective for the FAS task due to their ability to capture long-range dependencies among image patches. However, adaptive modules or auxiliary loss functions are often required to adapt pre-trained ViT weights learned on large-scale datasets such as ImageNet. In this work, we first show that initializing ViTs with multimodal (e.g., CLIP) pre-trained weights improves generalizability for the FAS task, which is in line with the zero-shot transfer capabilities of vision-language pre-trained (VLP) models. We then propose a novel approach for robust cross-domain FAS by grounding visual representations with the help of natural language. Specifically, we show that aligning the image representation with an ensemble of class descriptions (based on natural language semantics) improves FAS generalizability in low-data regimes. Finally, we propose a multimodal contrastive learning strategy to boost feature generalization further and bridge the gap between source and target domains. Extensive experiments on three standard protocols demonstrate that our method significantly outperforms the state-of-the-art methods, achieving better zero-shot transfer performance than five-shot transfer of “adaptive ViTs”.
CLIP is the first multimodal (in this case, vision and text) model tackling computer vision and was recently released by OpenAI on January 5, 2021….CLIP is a bridge between computer vision and natural language processing.
Sadly, Brems didn’t address ViT, so I turned to Chinmay Bhalerao.
Vision Transformers work by first dividing the image into a sequence of patches. Each patch is then represented as a vector. The vectors for each patch are then fed into a Transformer encoder. The Transformer encoder is a stack of self-attention layers. Self-attention is a mechanism that allows the model to learn long-range dependencies between the patches. This is important for image classification, as it allows the model to learn how the different parts of an image contribute to its overall label.
The output of the Transformer encoder is a sequence of vectors. These vectors represent the features of the image. The features are then used to classify the image.
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.
Bredemarket 