Identity/biometrics/technology marketing and writing services
If you’re wearing gloves, a biometric system can’t capture your fingerprints.
If you’re looking away from the camera, a biometric system can’t capture your face.
These are extreme examples of “failure to acquire,” but some systems don’t even account for these cases.
We commonly believe that modern people enjoy an abundance of data that historical people did not have. While this is often true, sometimes it isn’t.
Let’s look at the images we use in facial recognition.
ISO/IEC 19794-5 (Face image data) recommends a minimum inter-eye distance of 90 pixels.
But imagine for the moment that facial recognition existed 100 years ago. Could century-old film cameras achieve the necessary resolution to process faces on adding machines or whatever?
The answer is yes. Easily.
Back in the Roaring ‘20s, photographs of course were not digital images, but were captured and stored on film. During the 1920s a new film standard, 35mm film, was starting to emerge. And if you translate the “grains” in film to modern pixels, your facial image resolution is more than sufficient.
Here is what FilmFix says:
“Thirty-five-millimeter film has a digital resolution equivalent to approximately 5.6K — a digital image size of about 5,600 × 3,620 pixels.”
Yeah, that will work—considering that the Google Gemini image illustrating this post was generated at only 1,024 x 1,024 pixels.
An interesting item popped up in SAM.gov. According to a Request for Information (RFI) due February 20, the FBI may have interest in a system for secret biometric searches.
“The FBI intends to identify available software solutions to store and search subjects at the classified level. This solution is not intended to replace the Next Generation Identification System Functionality, which was developed and implemented in collaboration with the FBI’s federal, state, local, tribal, and territorial partners. The solution shall reside at the Secret and/or Top-Secret/SCI level with the ability to support data feeds from external systems. The solution must allow the ability to enroll and search face, fingerprint, palmprint, iris, and latent fingerprints, and associated biographic information with a given set of biometrics.”
Now remember that the Next Generation Identification (NGI) system is protected from public access by requiring all users to adhere to the CJIS Security Requirements. But the CJIS Security Requirements aren’t Secret or Top Secret. These biometric searches, whatever they are, must REALLY be kept from prying eyes.
The RFI itself is 8 pages long, and is mysteriously numbered as RFI 01302025. I would have expected an RFI number 01152026. I believe this was an editing error, since FBI RFI 01302025 was issued in 2025 for a completely different purpose.
Whatever the real number is, the RFI is labeled “Classified Identity-Based Biometric System.” No acronym was specified, so I’m self-acronyming it as CIBS. Perhaps the system has a real acronym…but it’s secret.
If your company can support such a system from a business, technical, and security perspective, the due date is February 20 and questions are due by February 2. See SAM.gov for details.
The purpose of measuring quality should not be for measurement’s own sake. The purpose should be to inform people to make useful decisions.
In Germany, the Bundesamt für Sicherheit in der Informationstechnik (Federal Office for Information Security) has developed the Open Source Face Image Quality (OFIQ) standard.
Experienced biometric professionals can’t help but notice that the acronym OFIQ is similar to the acronym NFIQ (used in NFIQ 2), but the latter refers to the NIST FINGERPRINT image quality standard. NFIQ is also open source, with contributions from NIST and the German BSI, among others.
But NFIQ and OFIQ, while analyzing different biometric modalities, serve a similar purpose: to distinguish between good and bad biometric images.
But do these open source algorithms meaningfully measure quality?
Biometric Update alerted readers to the November 2025 study “On the Utility of the Open Source Facial Image Quality Tool for Facial Biometric Recognition in DHS Operations” (PDF).
Note the words “in DHS Operations,” which are crucial.
So which algorithms did DHS evaluate? We don’t know precisely.
“A total of 16 commercial face recognition systems were used in this evaluation. They are labeled in diagrams as COTS1 through COTS16….Each algorithm in this study was voluntarily submitted to the MdTF as
part of on-going biometric performance evaluations by its commercial entity.”
Usally MdTF rally participants aren’t disclosed, unless a participant discloses itself, like Paravision did after the 2022 Biometric Technology Rally.
“Paravision’s matching system alias in the test was ‘Miami.'”
So what did DHS find when it used OFIQ to evaluate images submitted to these 16 algorithms?
“We found that the OFIQ unified quality score provides extremely limited utility in the DHS use cases we investigated. At operationally relevant biometric thresholds, biometric matching performance was high and probe samples that were assessed as having very low quality by OFIQ still successfully matched to references using a variety of face recognition algorithms.”
Or in human words:
So, at least in DHS’ case, it makes no sense to use the OFIQ algorithm.
Your mileage may vary.
If you have questions, consult a biometric product marketing expert.
Or Will Smith. Just don’t make a joke about his wife.
(Part of the biometric product marketing expert series)
I previously mused about an alternative universe in which a single human body had ten (different) faces.
Facial recognition would be more accurate if biometric systems had ten faces to match. (Kind of like you-know-what.)
Well, now I’m getting ridiculous by musing about a person with one hundred faces for identification.
When I’m not musing about alternative universes with different biometrics, I’m helping identity/biometric firms market their products in this one.
And this frivolous exercise actually illustrates a significant difference between fingerprints and faces, especially in use cases where subjects submit all ten fingerprints but only a single face. The accuracy benefits are…well, they’re ten times more powerful.
Are there underlying benefits in YOUR biometric technology that you want to highlight? Bredemarket can help you do this. Book a free meeting with me, and I’ll ask you some questions to figure out where we can work together.
I made this available to someone else, so I’m making it available to you. If you’re interested in a non-branded clip of the ten faces, here it is below.
The complete branded version remains at https://bredemarket.com/2026/01/12/1012/
The question again: if a human body had ten faces, how accurate would facial recognition be?
And the companion question…well, you’ll have to go to the branded version to see that.
(Part of the biometric product marketing expert series)
Bredemarket reserves the right to revisit topics I visited before.
Imagine an alternative universe in which a single human body had ten (different) faces and only one finger.
Think about the ramifications.
Credit for this thought, not original to me, must still remain anonymous.
But if you would like to discuss your biometric marketing and writing needs with a biometric product marketing expert, fill out the “free 30 minute content needs assessment” form on the page linked below to schedule a free conversation.
(Part of the biometric product marketing expert series)
Continuing in my series of looks at biometric accuracy in an alternative universe.
If you need to market a biometric product that handles challenging conditions, book a free meeting with me at https://bredemarket.com/mark/
Will facial recognition ever become precise enough to distinguish between identical twins?
NIST investigated this in 2023 but did not continue the research. From the report:
These results show that identical twins and same-sex fraternal twins give outcomes that are inconsistent with the intended or expected behaviour from a face recognition algorithm.
Bredemarket works with a number of technologies, but it’s no secret that my primary focus is biometrics. After all, I call myself the “biometric product marketing expert,” having worked with friction ridge (fingerprint, palm print), face, iris, voice, and rapid DNA.
If I can help your biometric firm with your content, proposal, or analysis needs, schedule a free meeting with me to discuss how I can help.
Bredemarket