You’ve probably noticed that I’ve created a lot of Bredemarket videos lately.
My longer ones last a minute. That’s the length of a video I haven’t shared in the Bredemarket blog (it’s on Instagram) summarizing my client work over the last four years. My early July identity and Inland Empire reels are almost a minute long.
Researchers in Canada surveyed 2,000 participants and studied the brain activity of 112 others using electroencephalograms (EEGs). Microsoft found that since the year 2000 (or about when the mobile revolution began) the average attention span dropped from 12 seconds to eight seconds.
As many noted, a goldfish’s attention span is 9 seconds.
Some argue that the 8 second attention span is not universal and varies according to the task. For example, a 21 minute attention span has been recorded for drivers. If drivers had an 8 second attention span, we would probably all be dead by now.
But watching a video is not a life-or-death situation. Viewers will happily jump away if there’s no reason to watch.
So I have my challenge.
Ironically, I learned about the 8 second rule while watching a LinkedIn Learning course about the 3 minute rule. I haven’t finished the course yet, so I haven’t yet learned how to string someone along for 22.5 8-second segments.
I use both text generators (sparingly) and image generators (less sparingly) to artificially create text and images. But I encounter one image challenge that you’ve probably encountered also: bizarre misspellings.
This post includes an example, created in Google Gemini, that was created using the following prompt:
Create a square image of a library bookshelf devoted to the works authored by Dave Barry.
Now in the ideal world, my prompt would completely research Barry’s published titles, and the resulting image would include these book titles (such as Dave Barry Slept Here, one of the greatest history books of all time maybe or maybe not).
In the mediocre world, at least the book spines would include the words “Dave Barry.”
Why can’t your image generator spell words properly?
It always mystified me that AI-generated images had so many weird words, to the point where I wondered whether the AI was specifically programmed to misspell.
It wasn’t…but it wasn’t programmed to spell either.
TechCrunch recently published an article in which the title was so good you didn’t have to read the article itself. The title? “Why is AI so bad at spelling? Because image generators aren’t actually reading text.”
This is something that I pretty much forgot.
When I use an AI-powered text generator, it has been trained to respond to my textual prompts and create text.
When I use an AI-powered image generator, it has been trained to respond to my textual prompts and create images.
Two very different tasks, as noted by Asmelash Teka Hadgu, co-founder of Lesan and a fellow at the DAIR Institute.
“The diffusion models, the latest kind of algorithms used for image generation, are reconstructing a given input,” Hagdu told TechCrunch. “We can assume writings on an image are a very, very tiny part, so the image generator learns the patterns that cover more of these pixels.”
The algorithms are incentivized to recreate something that looks like what it’s seen in its training data, but it doesn’t natively know the rules that we take for granted — that “hello” is not spelled “heeelllooo,” and that human hands usually have five fingers.
For a long time, each ML (machine learning) model operated in one data mode – text (translation, language modeling), image (object detection, image classification), or audio (speech recognition).
However, natural intelligence is not limited to just a single modality. Humans can read and write text. We can see images and watch videos. We listen to music to relax and watch out for strange noises to detect danger. Being able to work with multimodal data is essential for us or any AI to operate in the real world.
So if we ask an image generator to create an image of a library bookshelf with Dave Barry works, it would actually display book spines with Barry’s actual titles.
So why doesn’t my Google Gemini already provide this capability? It has a text generator and it has an image generator: why not provide both simultaneously?
Because that’s EXPENSIVE.
I don’t know whether Google’s Vertex AI provides the multimodal capabilties I seek, where text in images is spelled correctly.
You may remember the May hoopla regarding amendments to Illinois’ Biometric Information Privacy Act (BIPA). These amendments do not eliminate the long-standing law, but lessen its damage to offending companies.
The General Assembly is expected to send the bill to Illinois Governor JB Pritzker within 30 days. Gov. Pritzker will then have 60 days to sign it into law. It will be immediately effective.
While the BIPA amendment has passed the Illinois House and Senate and was sent to the Governor, there is no indication that he has signed the bill into law within the 60-day timeframe.
A proposed class action claims Photomyne, the developer of several photo-editing apps, has violated an Illinois privacy law by collecting, storing and using residents’ facial scans without authorization….
The lawsuit contends that the app developer has breached the BIPA’s clear requirements by failing to notify Illinois users of its biometric data collection practices and inform them how long and for what purpose the information will be stored and used.
In addition, the suit claims the company has unlawfully failed to establish public guidelines that detail its data retention and destruction policies.
When marketing digital identity products secured by biometrics, emphasize that they are MORE secure and more private than their physical counterparts.
When you hand your physical driver’s license over to a sleazy bartender, they find out EVERYTHING about you, including your name, your birthdate, your driver’s license number, and even where you live.
When you use a digital mobile driver’s license, bartenders ONLY learn what they NEED to know—that you are over 21.
Any endeavor, scientific or non-scientific, tends to generate a host of acronyms that the practitioners love to use.
For people interested in fingerprint identification, I’ve written this post to delve into some of the acronyms associated with NIST MINEX testing, including ANSI, INCITS, FIPS, and PIV.
NIST was involved with fingerprints before NIST even existed. Back when NIST was still the NBS (National Bureau of Standards), it issued its first fingerprint interchange standard back in 1986. I’ve previously talked about the 1993 version of the standard in this post, “When 250ppi Binary Fingerprint Images Were Acceptable.”
But let’s move on to another type of interchange.
MINEX
It’s even more important that we define MINEX, which stands for Minutiae (M) Interoperability (IN) Exchange (EX).
You’ll recall that the 1993 (and previous, and subsequent) versions of the ANSI/NIST standard included a “Type 9” to record the minutiae generated by the vendor for each fingerprint. However, each vendor generated minutiae according to its own standard. Back in 1993 Cogent had its standard, NEC its standard, Morpho its standard, and Printrak its standard.
So how do you submit Cogent minutiae to a Printrak system? There are two methods:
First, you don’t submit them at all. Just ignore the Cogent minutiae, look at the Printrak image, and use an algorithm regenerate the minutiae to the Printrak standard. While this works with high quality tenprints, it won’t work with low quality latent (crime scene) prints that require human expertise.
The second method is to either convert the Cogent minutiae to the Printrak minutiae standard, or convert both standards into a common format.
The American National Standards Institute (ANSI) is a private, non-profit organization that administers and coordinates the U.S. voluntary standards and conformity assessment system. Founded in 1918, the Institute works in close collaboration with stakeholders from industry and government to identify and develop standards- and conformance-based solutions to national and global priorities….
ANSI is not itself a standards developing organization. Rather, the Institute provides a framework for fair standards development and quality conformity assessment systems and continually works to safeguard their integrity.
So ANSI, rather than creating its own standards, works with outside organizations such as NIST…and INCITS.
INCITS
Now that’s an eye-catching acronym, but INCITS isn’t trying to cause trouble. Really, they’re not. Believe me.
Back in 2004, INCITS worked with ANSI (and NIST, who created samples) to develop three standards: one for finger images (ANSI INCITS 381-2004), one for face recognition (ANSI INCITS 385-2004), and one for finger minutiae (ANSI INCITS 378-2004, superseded by ANSI INCITS 378-2009 (S2019)).
When entities used this vendor-agnostic minutiae format, then minutiae from any vendor could in theory be interchanged with those from any other vendor.
This came in handy when the FIPS was developed for PIV. Ah, two more acronyms.
FIPS and PIV
One year after the three ANSI INCITS standards were released, this happened (the acronyms are defined in the text):
Federal Information Processing Standard (FIPS) 201 entitled Personal Identity Verification of Federal Employees and Contractors establishes a standard for a Personal Identity Verification (PIV) system (Standard) that meets the control and security objectives of Homeland Security Presidential Directive-12 (HSPD-12). It is based on secure and reliable forms of identity credentials issued by the Federal Government to its employees and contractors. These credentials are used by mechanisms that authenticate individuals who require access to federally controlled facilities, information systems, and applications. This Standard addresses requirements for initial identity proofing, infrastructure to support interoperability of identity credentials, and accreditation of organizations issuing PIV credentials.
So the PIV, defined by a FIPS, based upon an ANSI INCITS standard, defined a way for multiple entities to create and support fingerprint minutiae that were interoperable.
But how do we KNOW that they are interoperable?
Let’s go back to NIST and MINEX.
Testing interoperability
So NIST ended up in charge of figuring out whether these interoperable minutiae were truly interoperable, and whether minutiae generated by a Cogent system could be used by a Printrak system. Of course, by the time MINEX testing began Printrak no longer existed, and a few years later Cogent wouldn’t exist either.
You can read the whole history of MINEX testing here, but for now I’m going to skip ahead to MINEX III (which occurred many years after MINEX04, but who’s counting?).
Like some other NIST tests we’ve seen before, vendors and other entities submit their algorithms, and NIST does the testing itself.
In this case, all submitters include a template generation algorithm, and optionally can include a template matching algorithm.
Then NIST tests each algorithm against every other algorithm. So the “innovatrics+0020” template generator is tested against itself, and is also tested against the “morpho+0115” algorithm, and all the other algorithms.
NIST then performs its calculations and comes up with summary values of interoperability, which can be sliced and diced a few different ways for both template generators and template matchers.
From NIST. Top 10 template generators (Ascending “Pooled 2 Fingers FNMR @ FMR≤10-2“) as of July 29, 2024.
And this test, like some others, is an ongoing test, so perhaps in a few months someone will beat Innovatrics for the top pooled 2 fingers spot.
Are fingerprints still relevant?
And entities WILL continue to submit to the MINEX III test. While a number of identity/biometric professionals (frankly, including myself) seem to focus on faces rather than fingerprints, fingers still play a vital role in biometric identification, verification, and authentication.
The cohesive suite of security and productivity solutions provided by an E5 licence can significantly streamline your technological landscape, doing away with a number of on-premises and SaaS tools.
While many organisations opt for the lower-cost E3 licence, they may find this soon requires a supplementary selection of single-solution tools from alternate vendors to patch gaps in its capabilities.
Too many solutions means confusion, an often-disjointed workflow, potential overlap and overspend, and crucially, increased security risk.
By consolidating your collaboration, productivity, automation, and security solutions into a single trusted vendor platform, IT management becomes simplified, redundant solutions can be axed, and ROI can be better measured.
The Microsoft E5 Security Components
So you get everything from a single source with no finger pointing. What could go wrong?
Plenty, according to those who still think of Microsoft as an evil empire.
Microsoft is making a compelling case to businesses to consolidate into the Microsoft umbrella of products. The ease of use, and financial motives just make too much sense. Now do those customers get a great IAM experience with that? Meh…kinda. Entra SSO is solid product, Active Directory/EntraID is solid, MIM…well….we don’t talk about MIM.
Microsoft Identity Manager
Well, I will talk about MIM, or Microsoft Identity Manager.
Microsoft Identity Manager (MIM) 2016 builds on the identity and access management capabilities of Forefront Identity Manager (FIM) 2010 and predecessor technologies. MIM provides integration with heterogeneous platforms across the datacenter, including on-premises HR systems, directories, and databases.
MIM augments Microsoft Entra cloud-hosted services by enabling the organization to have the right users in Active Directory for on-premises apps. Microsoft Entra Connect can then make available in Microsoft Entra ID for Microsoft 365 and cloud-hosted apps
But what of the argument that it’s better to get everything from one vendor? Other companies will tout their best-in-class products. While you’ll end up with a possibly disjointed solution, the work will get done more accurately.
In the end, it’s up to you. Do you want a single solution that is “good enough” and is already pre-made, or do you want to take the best solution from the best-in-class vendors and roll your own?
I remember the first computer I ever owned: a Macintosh Plus with a hard disk with a whopping 20 megabytes of storage space. And that hard disk held ALL my files, with room to spare.
For sake of comparison, the video at the end of this blog post would fill up three-quarters of that old hard drive. Not that the Mac would have any way to play that video.
And its 20 megabyte hard disk illustrates the limitations of those days. File storage was a precious commodity in the 1980s and 1990s, and we therefore accepted images that we wouldn’t even think about accepting today.
This affected the ways in which entities exchanged biometric information.
The 1993 ANSI/NIST standard
The ANSI/NIST standard for biometric data interchange has gone through several iterations over the years, beginning in 1986 when NIST didn’t even exist (it was called the National Bureau of Standards in those days).
Yes, FINGERPRINT information. No faces. No scars/marks/tattoos. signatures, voice recordings, dental/oral data, irises, DNA, or even palm prints. Oh, and no XML-formatted interchange either. Just fingerprints.
No logical record type 99, or even type 10
Back in 1993, there were only 9 logical record types.
For purposes of this post I’m going to focus on logical record types 3 through 6 and explain what they mean.
Type 3, Fingerprint image data (low-resolution grayscale).
Type 4, Fingerprint image data (high-resolution grayscale).
Type 5, Fingerprint image data (low-resolution binary).
Type 6, Fingerprint image data (high-resolution binary).
Image resolution in the 1993 standard
In the 1993 version of the ANSI/NIST standard:
“Low-resolution” was defined in standard section 5.2 as “9.84 p/mm +/- 0.10 p/mm (250 p/in +/- 2.5 p/in),” or 250 pixels per inch (250ppi).
The “high-resolution” definition in sections 5.1 and 5.2 was twice that, or “19.69 p/mm +/- 20 p/mm (500 p/in +/- 5 p/in.”
While you could transmit at these resolutions, the standard still mandated that you actually scan the fingerprints at the “high-resolution” 500 pixels per inch (500ppi) value.
Incidentally, this brings up an important point. The series of ANSI/NIST standards are not focused on STORAGE of data. They are focused on INTERCHANGE of data. They only provided a method for Printrak system users to exchange data with automated fingerprint identification systems (AFIS) from NEC, Morpho, Cogent, and other fingerprint system providers. Just interchange. Nothing more.
Binary and grayscale data in the 1993 standard
Now let’s get back to Types 3 through 6 and note that you were able to exchange binary fingerprint images.
Why the heck would fingerprint experts tolerate a system that transmitted binary images that latent fingerprint examiners considered practically useless?
Because they had to.
Storage and transmission constraints in 1993
Two technological constraints adversely affected the interchange of fingerprint data in 1993:
Storage space. As mentioned above, storage space was limited and expensive in the 1980s and the 1990s. Not everyone could afford to store detailed grayscale images with (standard section 4.2) “eight bits (256 gray levels)” of data. Can you imagine storing TEN ENTIRE FINGERS with that detail, at an astronomical 500 pixels per inch?
Transmission speed. There was another limitation enforced by the modems of the data. Did I mention that the ANSI/NIST standard was an INTERCHANGE standard? Well, you couldn’t always interchange your data via the huge 1.44 megabyte floppy disks of the day. Sometimes you had to pull your your trusty 14.4k or 28.8k modem and send the images over the telephone. Did you want to spend the time sending those huge grayscale images over the phone line?
So as a workaround, the ANSI/NIST standard allowed users to interchange binary (black and white) images to save disk space and modem transmission time.
And we were all delighted with the capabilities of the 1993 ANSI/NIST standard.
Until we weren’t.
The 2015 ANSI/NIST standard
The current standard, ANSI/NIST-ITL 1-2011 Update 2015, supports a myriad of biometric types. For fingerprints (and palm prints), the focus is on grayscale images: binary image Type 5 and Type 6 are deprecated in the current standard, and low-resolution Type 3 grayscale images are also deprecated. Even Type 4 is shunned by most people in favor of new friction ridge image types in which the former “high resolution” is now the lowest resolution that anyone supports:
This week has been a busy week in Bredemarket-land, including work on some of the following client projects:
Creating the first deliverable as part of a three-part series of deliverables.
Reworking that first deliverable for more precision.
Preparing to start work on the second deliverable.
Drafting a blog post for a client.
Gathering information for an email newsletter for a client.
Following up on a couple of consulting opportunities that take advantage of my identity/biometric expertise.
Creating a promotional reel based upon the grapes in my backyard. (Yet another reel. I plan to reveal it next week.)
Engaging in other promotional activities on Bredemarket’s key social media channels.
Plus I’ve been working on some non-Bredemarket deliverables and meetings with a significant time commitment.
But there’s one more Bredemarket deliverable that I haven’t mentioned—because I’m about to discuss it now.
The task
Without going into detail, a client required me to repurpose a piece of third-party government-authored (i.e. non-copyrighted) text, originally written for a particular market.
Shorten the text so it would be more attractive to the new market.
Simplify the presentation of the text to make it even more attractive to the new market.
The request was clear, and I’ve already completed the first draft of the text and am working on the second draft.
But I wanted to dive into the three steps above—not regarding this particular client writing project, but in a more general way.
Step 1: Rewrite
When you’ve worked in a lot of different industries, you learn that each industry has its own language, including things you say—and things you don’t say.
I’ll give you an example that doesn’t reflect the particular project I was working on, but does reflect why rewriting is often necessary.
When I started in biometrics, the first two industries that I wrote about were law enforcement and benefits administration.
Law enforcement’s primary purpose is to catch bad people, although sometimes it can exonerate good people. So when you’re talking about law enforcement applications, you frequently use a lot of terms that are negative in nature, such as “surveillance,” “suspect,” and “mugshot.”
Benefits administration’s primary purpose is to help good people, although sometimes it can catch bad people who steal benefits from good people. So when you’re talking about benefits administration applications, you tend toward more positive terms such as “beneficiary.” And if you take a picture of a beneficiary’s face, for heaven’s sake DON’T REFER TO THE FACIAL IMAGE AS A “MUGSHOT.”
These two examples illustrate why something originally written for “market 1” must often be rewritten for “market 2.”
But sometimes a simple rewrite isn’t enough.
Step 2: Shorten
Now I don’t play in the B2C market in which crisp text is extremely necessary. But it’s needed in the various B2G and B2B markets also—some more than others.
If you are writing for more scientific markets, your readers are more accustomed to reading long, academic, “Sage”-like blocks of text.
But if you are writing for other markets, such as hospitality, your readers not only don’t want to read long blocks of text, but actively despise it.
You need to “get to the point.”
Tim Conway (Sr.), as repeatedly played during Jim Healy’s old radio show. Sourced from the Jim Healy Tribute Site.
In my particular project, “market 1” was one of those markets that valued long-windedness, while “market 2” clearly didn’t. So I had to cut the text down significantly, using the same techniques that I use when rewriting my “draft 0.5” (which a client NEVER sees) to my “draft 1” (which I turn over to the client).
But sometimes a simple shorten isn’t enough.
Step 3: Simplify
If you know me, you know I’m not graphically inclined.
Someday I will reach this level of graphic creativity. Originally created by Jleedev using Inkscape and GIMP. Redrawn as SVG by Ben Liblit using Inkscape. – Own work, Public Domain, link.
But I still pay attention to the presentation of my words.
Remember those long blocks of text that I mentioned earlier? One way to break them up is to use bullets.
Bullets break up long blocks of text into manageable chunks.
Bullets are easier to read.
So your reader will be very happy.
But as I was editing this particular piece of content, sometimes I ran into long lists of bullets, which weren’t really conducive to the reading experience.
Question
Answer
What does this mean?
Why are long lists of bullets bad?
Because with enough repetition, they’re just as bad as long blocks of text.
Your readers will tune you out.
How can you format long lists of bullets into something easier to read?
One way is to convert the bullets into a table with separate entries.
Your readers will enjoy a more attractive presentation.
What do tables do for your reader?
They arrange the content in two dimensions rather than one.
The readers’ eyes move in two directions, rather than just one.
Hey, wait a minute…
Yeah, I just plugged my seven questions again by intentionally using the first three: why, how, and what.
You can go here to download the e-book “Seven Questions Your Content Creator Should Ask You.”
I don’t have the skill to make WordPress tables look as attractive as Microsoft Word tables. But even this table breaks up the monotony of paragraphs and lists, don’t you think?
So what happened?
After I had moved through the three steps of rewriting, shortening, and simplifying the original content, I had a repurposed piece of content that was much more attractive to the “hungry people” (target audience) who were going to read it.
These people wouldn’t fall asleep while reading the content, and they wouldn’t be offended by some word that didn’t apply to them (such as “mugshot”).
So don’t be afraid to repurpose—even for a completely different market.
I do it all the time.
Look at two of my recent reels. Note the differences. But note the similarities.
The identity/biometrics version of the reel.
The Inland Empire version of the same reel.
So which of Bredemarket’s markets do you think will receive the “grapes” reel?
I wanted to share the latter on NextDoor, but that service wouldn’t accept the video.
Thinking the 45 second length was the issue, I decided to create a 15 second version of the Inland Empire video…and a 15 second version of the (50 second) identity/biometrics video while I was at it.
For those of you who would like to”a nice surprise…every once in a while.”
Identity/biometric.
Inland Empire.
By the way, I’m considering creating a new Inland Empire video…with an agricultural theme. (Fruits, not cows.)
Bredemarket 