Communicate with the Words of Authority

Biometric marketing leaders, do your firm’s product marketing publications require the words of authority?

John E. Bredehoft of Bredemarket, the biometric product marketing expert.

Can John E. Bredehoft of Bredemarket—the biometric product marketing expert—contribute words of authority to your content, proposal, and analysis materials?

I offer:

  • 30 years of biometric experience, 10 years of product marketing expertise, and complementary proposal and product management talents.
  • Success with numerous biometric firms, including Incode, IDEMIA, MorphoTrak, Motorola, Printrak, and over a dozen biometric consulting clients.
  • Mastery of multiple biometric modalities: friction ridge (fingerprint, palm print), face, iris, voice, DNA.
  • Compelling CONTENT creation: blog posts, case studies and testimonials, LinkedIn articles and posts, white papers.
  • Winning PROPOSAL development: managing, writing, editing for millions of dollars of business for my firms.
  • Actionable ANALYSIS: strategic, market, product, competitive.

To embed Bredemarket’s biometric product marketing expertise within your firm, schedule a free meeting with me.

Make an impact.

Revisiting Friction Ridge

(Imagen 4)

(Part of the biometric product marketing expert series)

Due to renewed interest in my blog post on “Friction Ridge,” and its related Substack post, I’ve ventured back to the ridge.

Friction ridge, featuring Ricky Nelson.

If you missed my May writings, the friction ridges on fingerprints, palm prints, and elsewhere are used for everything from solving crimes to protecting smartphones.

If your biometric company offers a product that harnesses the power of friction ridges to identify people…do your prospects know about how your solution benefits them?

If your company is keeping quiet about your friction ridge solutions, let Bredemarket help you find your voice and spill your secrets to your buying prospects. 

Content for tech marketers.

Frictionless Friction Ridges and Other Biometric Modalities

I wanted to write a list of the biometric modalities for which I provide experience.

So I started my usual list from memory: fingerprint, face, iris, voice, and DNA.

Then I stopped myself.

My experience with skin goes way beyond fingerprints, since I’ve spent over two decades working with palm prints.

(Can you say “Cambridgeshire method”? I knew you could. It was a 1990s method to use the 10 standard rolled fingerprint boxes to input palm prints into an automated fingerprint identification system. Because Cambridgeshire had a bias to action and didn’t want to wait for the standards folks to figure out how to enter palm prints. But I digress.)

So instead of saying fingerprints, I thought about saying friction ridges.

But there are two problems with this.

First, many people don’t know what “friction ridges” are. They’re the ridges that form on a person’s fingers, palms, toes, and feet, all of which can conceivably identify individuals.

But there’s a second problem. The word “friction” has two meanings: the one mentioned above, and a meaning that describes how biometric data is captured.

No, there is not a friction method to capture faces.
From https://www.youtube.com/watch?v=4XhWFHKWCSE.

No, there is not a friction method to capture faces. Squishing 

  • If you have to do something to provide your biometric data, such as press your fingers against a platen, that’s friction.
  • If you don’t have to do anything other than wave your fingers, hold your fingers in the air, or show your face as you stand near or walk by a camera, that’s frictionless.

More and more people capture friction ridges with frictionless methods. I did this years ago using MorphoWAVE at MorphoTrak facilities, and I did it today at Whole Foods Market.

So I could list my biometric modalities as friction ridge (fingerprint and palm print via both friction and frictionless capture methods), face, iris, voice, and DNA.

But I won’t.

Anyway, if you need content, proposal, or analysis assistance with any of these modalities, Bredemarket can help you. Book a meeting at https://bredemarket.com/cpa/

Amazon One Biometrics Worked. Maybe.

Because of the long line at the Upland Amazon Fresh, I didn’t want to wait around to activate my new Amazon One account. So I went to the Whole Foods on the other side of town. Then the fun began.

I had previously designated a payment card to use with Amazon One (Card #1). When I went to check out and provided my palm, I was asked to insert this card.

The reader said there was a problem with this card, so I inserted a different card (Card #2) and the payment processed on that card.

After my purchase I went back to my Amazon One app…which still showed Card #1 as my purchase card.

Not sure what’s going on.

Enrolling in the Amazon One Palm System via Smartphone

I think I’ve already mentioned that the Amazon Fresh in Upland, California is holding its grand opening in about an hour.

So I figured I should pre-investigate what was necessary to enroll in the Amazon One palm vein system once I arrived at the store.

My first discovery was that Amazon One has its own app, separate from the Amazon app. I don’t know how many apps Amazon has, but if Amazon and Meta ever merge (Amameta?), I will need a separate phone just for its apps.

So I downloaded Amazon One, linked it to my Amazon account, and waited for the instructions on how to enroll my palm at an Amazon location…

…only to find that Amazon One wanted to take pictures of both my palms, right there on my smartphone. Just like any contactless fingerprint app.

Enrolled in Amazon One.

So I am now enrolled, and I have confirmed that my local Amazon Fresh accepts Amazon Go.

Um…that is not East Foothill.

However, as even non-locals will realize, this is NOT 235 East Foothill, but WEST Foothill. So much for geolocation. (And the location of the Madonna of the Trail statue is wrong also, but I digress.)

Now let’s see if it all works.

Amazon in Upland: Is a Bear a Non-Person Entity?

So the day approaches, and the Upland Amazon Fresh will hold its grand opening on Thursday, May 1.

Amazon Fresh, Upland, California.

Wonder if the bear will show up.

Image from https://abc7.com/amazon-driver-bear-delivery-in-upland-caught-on-video/11503470/

What? You forgot about the bear?

“Yes, that’s an Amazon driver in the foreground, raising his hands to try to scare a bear away so he can make his delivery. He was successful. 

“The full Storyful video can be found here. (And of course it’s a Ring video. You didn’t expect a Nest video, did you?)”

I wonder if the bear’s paw will work with the palm vein reader.

Imagen 3. What’s the Amazon One error rate for THIS demographic group?

By the way, this is a reminder that Bredemarket provides its services to local Inland Empire businesses also. I can offer

  • compelling content creation
  • winning proposal development
  • actionable analysis

If Bredemarket can help your stretched staff, book a free meeting with me: https://bredemarket.com/cpa/

And one more thing…

After I wrote the main body of this post, I realized that I accidentally wrote the Bredemarket trifecta, covering all three of my concentrations:

  • Identity (Amazon)
  • Technology (Amazon)
  • Inland Empire (Amazon)

If you’re concerned about Amazon taking over everything, don’t fear. It will eventually fail.

But until it does, I’m gonna make some money!

#fakefakefake

Revisiting Amazon One

Because my local Amazon Fresh post is taking off, it’s a good time to revisit the “one” thing Uplanders will encounter when they get there.

I’ve talked about Amazon One palm/vein biometrics several times in the past.

Meanwhile, Amazon One is available at over 400 U.S. locations, with more on the way.

And it’s also available (or soon will be) on TP-Link door locks. But the How-To Geek writer is confused:

“TP-Link says that these palm vein patterns are so unique that they can even tell the difference between identical twins, making them safer than regular fingerprint or facial recognition methods.”

Um…fingerprints? Must be a Columbia University grad.

And the TP-Link page for the product has no sales restrictions. Even Illinois residents can buy it. Presumably there’s an ironclad consent agreement with every enrollment to prevent BIPA lawsuits.

(Picture from Imagen 3)

Baby Steps Toward Order of Magnitude Increases in Fingerprint Resolution

(Part of the biometric product marketing expert series)

For many years, the baseline for high-quality capture of fingerprint and palm print images has been to use a resolution of 500 pixels per inch. Or maybe 512 pixels per inch. Whatever.

The crime scene (latent) folks weren’t always satisfied with this, so they pushed to capture latent fingerprint and latent palm print images at 1000 pixels per inch. Pardon me, 1024.

But beyond this, the resolution of captured prints hasn’t really changed in decades. I’m sure some people have been capturing prints at 2000 (2048) pixels per inch, but there aren’t massive automated biometric identification systems that fully support this resolution from end to end.

But that may be changing.

One important truth about infant fingerprints

For about as long as latent examiners have pursued 1000 ppi print capture, people outside of the criminal justice arena have been looking at fingerprints for a very different purpose.

Our normal civil fingerprint processes require us to identify people via fingerprints beginning at the age of 18, or perhaps at the age of 12.

But gow do we identify people in those first 12 years?

More specifically, can we identify someone via their fingerprints at birth, and then authenticate them as an adult by comparing to those original prints?

It’s a dream, but many have pursued this dream. Dr. Anil Jain at Michigan State University has pursued this for years, and co-authored a 2014 paper on the topic.

Given that children, as well as the adults, in low income countries typically do not have any form of identification documents which can be used for this purpose [vaccination], we address the following question: can fingerprints be effectively used to recognize children from birth to 4 years? We have collected 1,600 fingerprint images (500 ppi) of 20 infants and toddlers captured over a 30-day period in East Lansing, Michigan and 420 fingerprints of 70 infants and toddlers at two different health clinics in Benin, West Africa.

At the time, it probably made sense to use 500 pixel per inch scanners to capture the prints, since developing countries don’t have a lot of money to throw around on expensive 1000 ppi scanners. But the use of regular scanners runs counter to a very important truth about infants and their fingerprints. Are you sitting down?

Because infants are smaller than adults, infant fingerprints are smaller than adult fingerprints.

Think about it. The standard FBI fingerprint card assumes that a rolled fingerprint occupies 1.6 inches x 1.5 inches of space. If you were to roll an infant fingerprint, it would occupy much less than that. Heck, I don’t even know if an infant’s entire FINGER is 1.6 inches long.

So the capture device is obtaining these teeny tiny ridges, and these teeny tiny ridge endings, and these teeny tiny bifurcations. Or trying to. And if those second-level details can’t be captured, then you’re not going to get the minutiae, and your fingerprint matching is going to fail.

So a decade later, researchers today are adopting a newer approach, according to a Biometric Update summary of an ID4Africa webinar. (This particular portion is at the very end of the webinar, at around the 2 hour 40 minute mark.)

A video presentation from Judge Lidia Maejima of the Court of Justice of Parana, Brazil introduced the emerging legal framework for biometric identification of infants. Her representative Felipe Hay explained how researchers in Brazil developed 5,000 dpi scanners, he says, which accurately record the minutiae of infants’ fingerprints.

Did you capture that? We’re moving from five hundred pixels per inch to FIVE THOUSAND pixels per inch. (Or maybe 5120.) Whether even that resolution is capable of capturing infant fingerprint detail remains to be seen.

And as Dr. Joseph Atick noted, all this research is still in its…um…infancy. We won’t know for years whether the algorithms can truly match infant fingerprints to child or adult fingerprints.

By the way, when talking about digital images, Adobe notes that the correct term is pixels per inch, not dots per inch. DPI specifically refers to printer resolution, which is appropriate when you’re printing a fingerprint card but not when you’re displaying an image on a screen.

(Image from From https://nvlpubs.nist.gov/nistpubs/SpecialPublications/NIST.SP.500-290e3.pdf )

Amazon One and Palm/Vein Identity Scanning in Healthcare: Does It Work?

If you create your own test data, you’re more likely to pass the test. So what data was used for Amazon One palm/vein identity scanning accuracy testing?

(Part of the biometric product marketing expert series)

(Image from Imagen 3)

I’ve previously discussed Amazon’s biometric palm/vein identity scanning efforts. But according to Dr. Sai Balasubramanian, M.D., J.D. in Forbes, Amazon is entering a new market, healthcare.

“Amazon announced that it is partnering with NYU Langone to launch Amazon One, a contactless palm screening technology, throughout the health system.”

Which makes sense, as long as the medical professional isn’t wearing gloves. I don’t know if Amazon One can read veins through medical gloves.

As I reflected upon this further, I realized something:

  • NIST has tested fingerprint verification and identification.
  • NIST has tested facial recognition. (Not that Amazon participated.)
  • NIST has tested iris recognition.

But NIST has never conducted regular testing of palm identification in general, or palm/vein identity scanning in particular. Not for Amazon. Not for Fujitsu. Not for Imprivata. Not for Ingenico. Not for Pearson. Not for anybody.

So how do we know that Amazon One works?

Because Amazon said so.

“Amazon One is 100 times more accurate than scanning two irises. It raises the bar for biometric identification by combining palm and vein imagery, and after millions of interactions among hundreds of thousands of enrolled identities, we have not had a single false positive.”

Claims may dazzle some people, but (as of 2023) Jim Nash was not among them:

“The company claims it is 99.999 percent accurate but does not offer information supporting that statistic.”

And so far I haven’t found any either.

Since the company trains its algorithm on synthetically generated palms, I would like to make sure the company performs its palm/vein identity scanning accuracy testing on REAL palms. If you actually CREATE the data for any test, including an accuracy test, there’s a higher likelihood that you will pass.

I think many people would like to see public substantiated Amazon One accuracy data. ZERO false positives is a…BOLD claim to make.