Tag Archives: facial recognition

Faulty “journalism” conclusions: the Israeli “master faces” study DIDN’T test ANY commercial biometric algorithms

(Part of the biometric product marketing expert series)

Modern “journalism” often consists of reprinting a press release without subjecting it to critical analysis. Sadly, I see a lot of this in publications, including both biometric and technology publications.

This post looks at the recently announced master faces study results, the datasets used (and the datasets not used), the algorithms used (and the algorithms not used), and the (faulty) conclusions that have been derived from the study.

Oh, and it also informs you of a way to make sure that you don’t make the same mistakes when talking about biometrics.

Vulnerabilities from master faces

From https://www.facebook.com/UplandPolice/photos/pcb.5828261687245780/5828261513912464/

In facial recognition, there is a concept called “master faces” (similar concepts can be found for other biometric modalities). The idea behind master faces is that such data can potentially match against MULTIPLE faces, not just one. This is similar to a master key that can unlock many doors, not just one.

This can conceivably happen because facial recognition algorithms do not match faces to faces, but match derived features from faces to derived features from faces. So if you can create the right “master” feature set, it can potentially match more than one face.

However, this is not just a concept. It’s been done, as Biometric Update informs us in an article entitled ‘Master faces’ make authentication ‘extremely vulnerable’ — researchers.

Ever thought you were being gaslighted by industry claims that facial recognition is trustworthy for authentication and identification? You have been.

The article goes on to discuss an Israeli research project that demonstrated some true “master faces” vulnerabilities. (Emphasis mine.)

One particular approach, which they write was based on Dlib, created nine master faces that unlocked 42 percent to 64 percent of a test dataset. The team also evaluated its work using the FaceNet and SphereFace, which like Dlib, are convolutional neural network-based face descriptors.

They say a single face passed for 20 percent of identities in Labeled Faces in the Wild, an open-source database developed by the University of Massachusetts. That might make many current facial recognition products and strategies obsolete.

Sounds frightening. After all, the study not only used dlib, FaceNet, and SphereFace, but also made reference to a test set from Labeled Faces in the Wild. So it’s obvious why master faces techniques might make many current facial recognition products obsolete.

Right?

Let’s look at the datasets

It’s always more impressive to cite an authority, and citations of the University of Massachusetts’ Labeled Faces in the Wild (LFW) are no exception. After all, this dataset has been used for some time to evaluate facial recognition algorithms.

But what does Labeled Faces in the Wild say about…itself? (I know this is a long excerpt, but it’s important.)

DISCLAIMER:

Labeled Faces in the Wild is a public benchmark for face verification, also known as pair matching. No matter what the performance of an algorithm on LFW, it should not be used to conclude that an algorithm is suitable for any commercial purpose. There are many reasons for this. Here is a non-exhaustive list:

Face verification and other forms of face recognition are very different problems. For example, it is very difficult to extrapolate from performance on verification to performance on 1:N recognition.

Many groups are not well represented in LFW. For example, there are very few children, no babies, very few people over the age of 80, and a relatively small proportion of women. In addition, many ethnicities have very minor representation or none at all.

While theoretically LFW could be used to assess performance for certain subgroups, the database was not designed to have enough data for strong statistical conclusions about subgroups. Simply put, LFW is not large enough to provide evidence that a particular piece of software has been thoroughly tested.

Additional conditions, such as poor lighting, extreme pose, strong occlusions, low resolution, and other important factors do not constitute a major part of LFW. These are important areas of evaluation, especially for algorithms designed to recognize images “in the wild”.

For all of these reasons, we would like to emphasize that LFW was published to help the research community make advances in face verification, not to provide a thorough vetting of commercial algorithms before deployment.

While there are many resources available for assessing face recognition algorithms, such as the Face Recognition Vendor Tests run by the USA National Institute of Standards and Technology (NIST), the understanding of how to best test face recognition algorithms for commercial use is a rapidly evolving area. Some of us are actively involved in developing these new standards, and will continue to make them publicly available when they are ready.

So there are a lot of disclaimers in that text.

  • LFW is a 1:1 test, not a 1:N test. Therefore, while it can test how one face compares to another face, it cannot test how one face compares to a database of faces. The usual law enforcement use case is to compare a single face (for example, one captured from a video camera) against an entire database of known criminals. That’s a computationally different exercise from the act of comparing a crime scene face against a single criminal face, then comparing it against a second criminal face, and so forth.
  • The people in the LFW database are not necessarily representative of the world population, the population of the United States, the population of Massachusetts, or any population at all. So you can’t conclude that a master face that matches against a bunch of LFW faces would match against a bunch of faces from your locality.
  • Captured faces exhibit a variety of quality levels. A face image captured by a camera three feet from you at eye level in good lighting will differ from a face image captured by an overhead camera in poor lighting. LFW doesn’t have a lot of these latter images.

I should mention one more thing about LFW. The researchers allow testers to access the database itself, essentially making LFW an “open book test.” And as any student knows, if a test is open book, it’s much easier to get an A on the test.

By MCPearson – Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=25969927

Now let’s take a look at another test that was mentioned by the LFW folks itself: namely, NIST’s Face Recognition Vendor Test.

This is actually a series of tests that has evolved over the years; NIST is now conducting ongoing tests for both 1:1 and 1:N (unlike LFW, which only conducts 1:1 testing). This is important because most of the large-scale facial recognition commercial applications that we think about are 1:N applications (see my example above, in which a facial image captured at a crime scene is compared against an entire database of criminals).

In addition, NIST uses multiple data sets that cover a number of use cases, including mugshots, visa photos, and faces “in the wild” (i.e. not under ideal conditions).

It’s also important to note that NIST’s tests are also intended to benefit research, and do not necessarily indicate that a particular algorithm that performs well for NIST will perform well in a commercial implementation. (If the algorithm is even available in a commercial implementation: some of the algorithms submitted to NIST are research algorithms only that never made it to a production system.) For the difference between testing an algorithm in a NIST test and testing an algorithm in a production system, please see Mike French’s LinkedIn article on the topic. (I’ve cited this article before.)

With those caveats, I will note that NIST’s FRVT tests are NOT open book tests. Vendors and other entities give their algorithms to NIST, NIST tests them, and then NIST tells YOU what the results were.

So perhaps it’s more robust than LFW, but it’s still a research project.

Let’s look at the algorithms

Now that we’ve looked at two test datasets, let’s look at the algorithms themselves and evaluate the claim that results for the three algorithms Dlib, FaceNet, and SphereFace can naturally be extrapolated to ALL facial recognition algorithms.

This isn’t the first time that we’ve seen such an attempt at extrapolation. After all, the MIT Media Lab’s Gender Shades study (which evaluated neither 1:1 nor 1:N use cases, but algorithmic attempts to identify gender and race) itself only used three algorithms. Yet the popular media conclusion from this study was that ALL facial recognition algorithms are racist.

Compare this with NIST’s subsequent study, which evaluated 189 algorithms specially for 1:1 and 1:N use cases. While NIST did find some race/sex differences in algorithms, these were not universal: “Tests showed a wide range in accuracy across developers, with the most accurate algorithms producing many fewer errors.”

In other words, just because an earlier test of three algorithms demonstrated issues in determining race or gender, that doesn’t mean that the current crop of hundreds of algorithms will necessarily demonstrate issues in identifying individuals.

So let’s circle back to the master faces study. How do the results of this study affect “current facial recognition products”?

The answer is “We don’t know.”

Has the master faces experiment been duplicated against the leading commercial algorithms tested by Labeled Faces in the Wild? Apparently not.

Has the master faces experiment been duplicated against the leading commercial algorithms tested by NIST? Well, let’s look at the various ways you can define the “leading” commercial algorithms.

For example, here’s the view of the test set that IDEMIA would want you to see: the 1:N test sorted by the “Visa Border” column (results as of August 6, 2021):

From https://pages.nist.gov/frvt/html/frvt1N.html as of August 6, 2021.

And here’s the view of the test set that Paravision would want you to see: the 1:1 test sorted by the “Mugshot” column (results as of August 6, 2021):

From https://pages.nist.gov/frvt/html/frvt11.html as of August 6, 2021.

Now you can play with the sort order in many different ways, but the question remains: have the Israeli researchers, or anyone else, performed a “master faces” test (preferably a 1:N test) on the IDEMIA, Paravision, Sensetime, NtechLab, Anyvision, or ANY other commercial algorithm?

Maybe a future study WILL conclude that even the leading commercial algorithms are vulnerable to master face attacks. However, until such studies are actually performed, we CANNOT conclude that commercial facial recognition algorithms are vulnerable to master face attacks.

So naturally journalists approach the results critically…not

But I’m sure that people are going to make those conclusions anyway.

From https://xkcd.com/386/. Attribution-NonCommercial 2.5 Generic (CC BY-NC 2.5).

Does anyone even UNDERSTAND these studies? (Or do they choose NOT to understand them?)

How can you avoid the same mistakes when communicating about biometrics?

As you can see, people often write about biometric topics without understanding them fully.

Even biometric companies sometimes have difficulty communicating about biometric topics in a way that laypeople can understand. (Perhaps that’s the reason why people misconstrue these studies and conclude that “all facial recognition is racist” and “any facial recognition system can be spoofed by a master face.”)

Are you about to publish something about biometrics that requires a sanity check? (Hopefully not literally, but you know what I mean.)

Well, why not turn to a biometric content marketing expert? Use the identity/biometric blog expert to write your blog post, the identity/biometric case study expert to write your case study, or the identity/biometric white paper expert to…well, you get the idea. (And all three experts are the same person!)

Bredemarket offers over 25 years of experience in biometrics that can be applied to your marketing and writing projects.

If you don’t have a content marketing project now, you can still subscribe to my Bredemarket Identity Firm Services LinkedIn page or my Bredemarket Identity Firm Services Facebook group to keep up with news about biometrics (or about other authentication factors; biometrics isn’t the only one). Or scroll down to the bottom of this blog post and subscribe to my Bredemarket blog.

If my content creation process can benefit your biometric (or other technology) marketing and writing projects, contact me.

Maryland will soon deal with privacy stakeholders (and they CAN’T care about the GYRO method)

Just last week, I mentioned that the state of Utah appointed the Department of Government Operations’ first privacy officer. Now Maryland is getting into the act, and it’s worth taking a semi-deep dive into what Maryland is doing, and how it affects (or doesn’t affect) public safety.

By François Jouffroy – Christophe MOUSTIER (1994), Attribution, https://commons.wikimedia.org/w/index.php?curid=727606

According to Government Technology, the state of Maryland has created two new state information technology positions, one of which is the State Chief Privacy Officer. Because government, I will refer to this as the SCPO throughout the remainder of this post. If you are referring to this new position in verbal conversation, you can refer to the “Maryland skip-oh.” Or the “crab skip-oh.”

From https://teeherivar.com/product/maryland-is-for-crabs/. Fair use. Buy it if you like it. Virginians understand the origins of the phrase.

Governor Hogan announced the creation of the SCPO position via an Executive Order, a PDF of which can be found here.

Let me call out a few provisions in this executive order.

  • A.2. defines “personally identifiable information,” consisting of a person’s name in conjunction with other information, including but not limited to “[b]iometric information including an individual’s physiological or biological characteristics, including an individual’s deoxyribonucleic acid.” (Yes, that’s DNA.) Oh, and driver’s license numbers also.
  • At the same time, A.2 excludes “information collected, processed, or shared for the purposes of…public safety.”
  • But on the other hand, A.5 lists specific “state units” covered by certain provisions of the law, including both The Department of Public Safety and Correctional Services and the Department of State Police.
  • The reason for the listing of the state units is because every one of them will need to appoint “an agency privacy official” (C.2) who works with the SCPO.

There are other provisions, including the need for agency justification for the collection of personally identifiable information (PII), and the need to provide individuals with access to their collected PII along with the ability to correct or amend it.

But for law enforcement agencies in Maryland, the “public safety” exemption pretty much limits the applicability of THIS executive order (although other laws to correct public safety data would still apply).

Therefore, if some Maryland sheriff’s department releases an automated fingerprint identification system Request for Proposal (RFP) next month, you probably WON’T see a privacy advocate on the evaluation committee.

But what about an RFP released in 2022? Or an RFP released in a different state?

Be sure to keep up with relevant privacy legislation BEFORE it affects you.

Pangiam, CLEAR, and others make a “sporting” effort to deny (or allow) stadium access

Back when I initially entered the automated fingerprint identification systems industry in the last millennium, I primarily dealt with two markets: the law enforcement market that seeks to solve crimes and identify criminals, and the welfare benefits market that seeks to make sure that the right people receive benefits (and the wrong people don’t).

Other markets simply didn’t exist. If I pulled out my 1994-era mobile telephone and looked at it, nothing would happen. Today, I need to look at my 2020-era mobile telephone to obtain access to its features.

And there are other biometric markets also.

Pangiam and stadium bans

Back in 1994 I couldn’t envision a biometrics story in Sports Illustrated magazine. But SI just ran a story on how facial recognition can be used to keep fans out of stadiums who shouldn’t be there.

Some fans (“fanatics”) perform acts in stadiums that cause the sports teams and/or stadium authorities to officially ban them from the stadium, sometimes for life.

John Green is the man in the blue shirt and white baseball cap to Artest’s left. By Copyright 2004 National Basketball Association. – Television broadcast of the Pacers-Pistons brawl on ESPN., Fair use, https://en.wikipedia.org/w/index.php?curid=6824157

But in the past, these measures were ineffective.

For a long time, those “measures” were limited at best. Fans do not have to show ID upon entering arenas. Teams could run checks on all the credit cards to purchase tickets to see whether any belonged to banned fans, but those fans could easily have a friend buy the tickets. 

But there are other ways to enforce stadium bans, and Sports Illustrated quoted an expert on the matter.

“They’ve kicked the fan out; they’ve taken a picture—that fan they know,” says Shaun Moore, CEO of a facial-recognition company called Trueface. “The old way of doing things was, you give that picture to the security staff and say, ‘Don’t let this person back in.’ It’s not really realistic. So the new way of doing it is, if we do have entry-level cameras, we can run that person against everyone that’s coming in. And if there’s a hit, you know then; then there’s a notification to engage with that person.”

This, incidentally, is an example of a “deny list,” or the use of a security system to deny a person access. We’ll get to that later.

But did you notice the company that was mentioned in the last quote? I’ve mentioned that company before, because Trueface was the most recent acquisition by the company Pangiam, a company that has also acquired airport security technology.

But Pangiam/Trueface isn’t the only company serving stadium (and entertainment) venues.

CLEAR and stadium entry

Most of the time, sports stadiums aren’t concentrating on the practice of DENYING people entry to a stadium. They make a lot more money by ALLOWING people entry to a stadium…and allowing them to enter as quickly as possible so they can spend money on concessions.

One such company that supports this is CLEAR, which was recently in the news because of its Initial Public Offering. Coincidentally, CLEAR also provides airport security technology, but it has branched out from that core market and is also active in other areas.

For example, let’s say you’re a die-hard New York Mets fan, and you head to Citi Field to watch a game.

By Chris6d – Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=101751795

The Mets don’t just let anyone into the stadium; you have to purchase a ticket. So you need to take your ticket out of your pocket and show it to the gate staff, or you need to take your smartphone out of your pocket and show your digital ticket to the gate staff.

What if you could get into the stadium without taking ANYTHING out of your pocket? Well, you can.

In the CLEAR Lane, your fingerprint is all you need to use tickets in your MLB Ballpark app – no need to pull out your phone or printed ticket as you enter the game.

Now that is really easy.

Pangiam and CLEAR aren’t the only companies in this space, as I well know. But there’s the possibility that biometrics will be used more often for access to sports games, concerts, and similar events.

Two articles on facial recognition

Within the last hour I’ve run across two articles that discuss various aspects of facial recognition, dispelling popular society notions about the science in the process.

Ban facial recognition? Ain’t gonna happen

The first article was originally shared by my former IDEMIA colleague Peter Kirkwood, who certainly understood the significance of it from his many years in the identity industry.

The article, published by the Security Industry Association (SIA), is entitled “Most State Legislatures Have Rejected Bans and Severe Restrictions on Facial Recognition.”

CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=664901

Admittedly the SIA is by explicit definition an industry association, but in this case it is simply noting a fact.

With most 2021 legislative sessions concluded or winding down for the year, proposals to ban or heavily restrict the technology have had very limited overall success despite recent headlines. It turns out that such bills failed to advance or were rejected by legislatures in no fewer than 17 states during the 2020 and 2021 sessions: California, Colorado, Hawaii, Kentucky, Maine, Maryland, Massachusetts, Michigan, Minnesota, Montana, Nebraska, New Hampshire, New Jersey, New York, Oregon, South Carolina and Washington.

And the article even cited one instance in which public safety and civil libertarians worked together, proving such cooperation is actually possible.

In March, Utah enacted the nation’s most comprehensive and precise policy safeguards for government applications. The measure, supported both by the Utah Department of Public Safety as well as the American Civil Liberties Union, establishes requirements for public-sector and law enforcement use, including conditions for access to identity records held by the state, and transparency requirements for new public sector applications of facial recognition technology.

This reminds me of Kirkwood’s statement when he originally shared the article on LinkedIn: “Targeted use with appropriate governance and transparency is an incredibly powerful and beneficial tool.”

NIST’s biometric exit tests reveal an inconvenient truth

Meanwhile, the National Institute of Standards and Technology, which is clearly NOT an industry association, continues to enhance its ongoing Facial Recognition Vendor Test (FRVT). As I noted myself on Facebook and LinkedIn:

With its latest rounds of biometric testing over the last few years, the National Institute of Standards and Technology has shown its ability to adapt its testing to meet current situations.

In this case, NIST announced that it has applied its testing to the not-so-new use case of using facial recognition as a “biometric exit” tool, or as a way to verify that someone who was supposed to leave the country has actually left the country. The biometric exit use case emerged after 9/11 in response to visa overstays, and while the vast, vast majority of people who overstay visas do not fly planes into buildings and kill thousands of people, visa overstays are clearly a concern and thus merit NIST testing.

Transportation Security Administration Checkpoint at John Glenn Columbus International Airport. By Michael Ball – Own work, CC0, https://commons.wikimedia.org/w/index.php?curid=77279000

But buried at the end of the NIST report (accessible from the link in NIST’s news release) was a little quote that should cause discomfort to all of those who reflexively believe that all biometrics is racist, and thus needs to be banned entirely (see SIA story above). Here’s what NIST said after having looked at the data from the latest test:

“The team explored differences in performance on male versus female subjects and also across national origin, which were the two identifiers the photos included. National origin can, but does not always, reflect racial background. Algorithms performed with high accuracy across all these variations. False negatives, though slightly more common for women, were rare in all cases.”

And as Peter Kirkwood and many other industry professionals would say, you need to use the technology responsibly. This includes things such as:

  • In criminal cases, having all computerized biometric search results reviewed by a trained forensic face examiner.
  • ONLY using facial recognition results as an investigative lead, and not relying on facial recognition alone to issue an arrest warrant.

So facial recognition providers and users had a good day. How was yours?

Is your home your castle when you use consumer doorbell facial recognition?

(Part of the biometric product marketing expert series)

For purposes of this post, I will define three entities that can employ facial recognition:

  • Public organizations such as governments.
  • Private organizations such as businesses.
  • Individuals.

Some people are very concerned about facial recognition use by the first two categories of entities.

But what about the third category, individuals?

Can individuals assert a Constitutional right to use facial recognition in their own homes? And what if said individuals live in Peoria?

Concerns about ANY use of facial recognition

Let’s start with an ACLU article from 2018 regarding “Amazon’s Disturbing Plan to Add Face Surveillance to Your Front Door.”

Let me go out on a limb and guess that the ACLU opposes the practice.

The article was prompted by an Amazon 2018 patent application which involved both its Rekognition facial recognition service and its Ring cameras.

One of the figures in Amazon’s patent application, courtesy the ACLU. https://www.aclunc.org/docs/Amazon_Patent.pdf

While the main thrust of the ACLU article concerns acquisition of front door face surveillance (and other biometric) information by the government, it also briefly addresses the entity that is initially performing the face surveillance: namely, the individual.

Likewise, homeowners can also add photos of “suspicious” people into the system and then the doorbell’s facial recognition program will scan anyone passing their home.

I should note in passing that ACLU author Jacob Snow is describing a “deny list,” which flags people who should NOT be granted access such as that pesky solar power salesperson. In most cases, consumer products tout the use of an “allow list,” which flags people who SHOULD be granted access such as family members.

Regardless of whether you’re discussing a deny list or an allow list, the thrust of the ACLU article isn’t that governments shouldn’t use facial recognition. The thrust of the article is that facial recognition shouldn’t be used at all.

The ACLU and other civil rights groups have repeatedly warned that face surveillance poses an unprecedented threat to civil liberties and civil rights that must be stopped before it becomes widespread.

Again, not face surveillance by governments, but face surveillance period. People should not have the, um, “civil liberties” to use the technology.

But how does the tech world approach this?

The reason that I cited that particular ACLU article was that it was subsequently referenced in a CNET article from May 2021. This article bore the title “The best facial recognition security cameras of 2021.”

Let me go out on a limb and guess that CNET supports the practice.

The last part of author Megan Wollerton’s article delves into some of the issues regarding facial recognition use, including those raised by the ACLU. But the bulk of the article talks about really cool tech.

As I stated above, Wollerton notes that the intended use case for home facial recognition security systems involves the creation of an “allow list”:

Some home security cameras have facial recognition, an advanced option that lets you make a database of people who visit your house regularly. Then, when the camera sees a face, it determines whether or not it belongs to someone in your list of known faces. If the recognition system does not know who is at the door, it can alert you to an unknown person on your property.

Obviously you could repurpose such a system for anything you want, provided that you can obtain a clear picture of the face of the pesky social power salesperson.

Before posting her reviews of various security systems, and after a brief mention (expanded later in the article) about possible governmental misuse of facial recognition, Wollerton redirects the conversation.

But let’s step back a bit to the consumer realm. Your home is your castle, and the option of having surveillance cameras with facial recognition software is still compelling for those who want to be on the cutting edge of smart home innovation.

“Your home is your castle” may be a distinctly American concept, but it certainly applies here as organizations such as, um, the ACLU defend a person’s right against unreasonable actions by governments.

Obviously, there are limits to ANY Constitutional right. I cannot exercise my Fourth Amendment right to be secure in my house, couple that with my First Amendment right to freely exercise my religion, and conclude that I have the unrestricted right to perform ritual child sacrifices in my home. (Although I guess if I have a home theater and only my family members are present, I can probably yell “Fire!” all I want.)

So perhaps I could mount an argument that I can use facial recognition at my house any time I want, if the government agrees that this right is “reasonable.”

But it turns out that other people are involved.

You knew I was going to mention Illinois in this post

OK, it’s BIPA time.

As I previously explained in a January 2021 post about the Kami Doorbell Camera, “BIPA” is Illinois’ Biometric Information Privacy Act. This act imposes constraints on a private entity’s use of biometrics. (Governments are excluded in Illinois BIPA.) And here’s how BIPA defines the term “private entity”:

“Private entity” means any individual, partnership, corporation, limited liability company, association, or other group, however organized. A private entity does not include a State or local government agency. A private entity does not include any court of Illinois, a clerk of the court, or a judge or justice thereof.

Did you see the term “individual” in that definition?

So BIPA not only affects company use of biometrics, such as use of biometrics by Google or by a theme park or by a fitness center. It also affects an individual such as Harry or Harriet Homeowner’s use of biometrics.

As republished at https://treeandneighborlawblog.com/2020/02/21/case-of-the-day-friday-february-21-2020/. Fair use.

As I previously noted, Google does not sell its Nest Cam “familiar face alert” feature in Illinois. But I guess it’s possible (via location spoofing if necessary) for someone to buy Nest Cam familiar face alerts in Indiana, and then sneak the feature across the border and implement it in the Land of Lincoln. But while this may (or may not) get Google off the hook, the individual is in a heap of trouble (should a trial lawyer decide to sue the individual).

Let’s face it. The average user of Nest Cam’s familiar face alerts, or the Kami Doorbell Camera, or any other home security camera with facial recognition (note that Amazon currently is not using facial recognition in its consumer products), is probably NOT complying with BIPA.

A private entity in possession of biometric identifiers or biometric information must develop a written policy, made available to the public, establishing a retention schedule and guidelines for permanently destroying biometric identifiers and biometric information when the initial purpose for collecting or obtaining such identifiers or information has been satisfied or within 3 years of the individual’s last interaction with the private entity, whichever occurs first.

I mean it’s hard enough for Harry and Harriet to get their teenage son to acknowledge receipt of the Homeowner family’s written policy for the use of the family doorbell camera. And you can forget about getting the pesky solar power salesperson to acknowledge receipt.

So from a legal perspective, it appears that any individual homeowner who installs a facial recognition security system can be hauled into civil court under BIPA.

But will these court cases be filed from a practical perspective?

Probably not.

When a social media company violates BIPA, the violation conceivably affects millions of individuals and can result in millions or billions of dollars in civil damages.

When the pesky solar power salesperson discovers that Harry and Harriet Homeowner, the damages would be limited to $1,000 or $5,000 plus relevant legal fees.

It’s not worth pursuing, any more than it’s worth pursuing the Illinois driver who is speeding down the expressway at 66 miles per hour.

My LinkedIn article “Don’t ban facial recognition”

By TapTheForwardAssist – Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=98670006

This post serves as a pointer to an article that I just published on LinkedIn, “Don’t ban facial recognition.”

If you’re going to prohibit use of a particular tool, you may want to check the alternatives to that tool to see if the alternatives are better…or worse.

To read the article, go here.

Pangiam acquires something else (in this case TrueFace)

People have been coming here to find this news (thanks Google Search Console) so I figured I’d better share it here.

Remember Pangiam, the company that I talked about back in March when it acquired the veriScan product from the Metropolitan Washington Airports Authority? Well, last week Pangiam acquired another company.

TYSONS CORNER, Va., June 2, 2021 /PRNewswire/ — Pangiam, a technology-based security and travel services provider, announced today that it has acquired Trueface, a U.S.-based leader in computer vision focused on facial recognition, weapon detection and age verification technologies. Terms of the transaction were not disclosed….

Trueface, founded in 2013 by Shaun Moore and Nezare Chafni, provides industry leading computer vision solutions to customers in a wide range of industries. The company’s facial recognition technology recently achieved a top three ranking among western vendors in the National Institute of Standards and Technology (NIST) 1:N Face Recognition Vendor Test. 

(Just an aside here: companies can use NIST tests to extract all sorts of superlatives that can be applied to their products, once a bunch of qualifications are applied. Pay attention to the use of the phrase “among western vendors.” While there may be legitimate reasons to exclude non-western vendors from comparisons, make a mental note when such an exclusion is made.)

But what does this mean in terms of Pangiam’s existing product? The press release covers this also.

Trueface will add an additional capability to Pangiam’s existing technologies, creating a comprehensive and seamless solution to satisfy the needs of both federal and commercial enterprises.

And because Pangiam is not a publicly-traded company, it is not obliged to add a disclaimer to investors saying this integration might not happen bla bla bla. Publicly traded companies are obligated to do this so that investors are aware of the risks when a company speculates about its future plans. Pangiam is not publicly traded, and the owners are (presumably) well aware of the risks.

For example, a US government agency may prefer to do business with an eastern vendor. In fact, the US government does a lot of business with one eastern vendor (not Chinese or Russian).

But we’ll see what happens with any future veriTruefaceScan product.

When biometric readers are “magic” (it’s a small face after all)

(Part of the biometric product marketing expert series)

The news coming across the wire is that Disney’s Magic Kingdom in Florida is testing facial recognition. (H/T International Biometrics + Identity Association.)

“At Walt Disney World Resort, we’re always looking for innovative and convenient ways to improve our guests’ experience—especially as we navigate the impact of COVID-19. With the future in mind and the shift in focus to more touchless experiences, we’re conducting a limited 30-day test using facial recognition technology.”

If the test is successful and facial recognition is implemented, it would be a replacement for (touch) fingerprint technology, which the Disney parks suspended last July for health reasons. (Although touchless fingerprint options are available.)

Disney’s biometric history extends back to 2006, when it used hand geometry.

Pangiam, a new/old player in biometric boarding

Make vs. buy.

Businesses are often faced with the question of whether to buy a product or service from a third party, or make the product or service itself.

And airports are no exception to this.

The Metropolitan Washington Airports Authority (MWAA), the entity that manages two of the airports in the Washington, DC area, needed a biometric boarding (biometric exit) solution. Such solutions allow passengers to skip the entire “pull out the paper ticket” process, or even the “pull out the smartphone airline app” process, and simply stand and let a camera capture a picture of the passenger’s face. While there are several companies that sell such solutions, MWAA decided to create its own solution, veriScan.

https://www.airportveriscan.com/

And once MWAA had implemented veriScan at its own airports, it started marketing the solution to other airports, and competing against other providers who were trying to sell their own solutions to airports.

Well, MWAA got out of the border product/service business last week when it participated in this announcement:

ALEXANDRIA, Va., March 19, 2021 /PRNewswire/ — Pangiam, a technology-based security and travel services provider, announced today that it has acquired veriScan, an integrated biometric facial recognition system for airports and airlines, from the Metropolitan Washington Airports Authority (“Airports Authority”). Terms of the transaction were not disclosed.

Pangiam is clearly the new kid on the block, since the company didn’t even exist in its current form a year ago. Late last year, AE Industrial Partners acquired and merged the decade-old Linkware and the newly-formed Pangian (PRE LLC) “to form a highly integrated travel solutions technology platform providing a more seamless and secure travel experience.”

But in a sense, Pangiam ISN’T new to the travel industry, once you read the biographies of many of the principals at the company.

  • “Most recently (Kevin McAleenan) served as Acting Secretary of the U.S. Department of Homeland Security (DHS)….”
  • “Prior to Pangiam, Patrick (Flanagan) held roles at U.S. Customs & Border Protection (CBP), the U.S. Navy, the National Security Staff, the Transportation Security Administration (TSA), and the Department of Homeland Security (DHS).”
  • “Dan (Tanciar) previously served as the Executive Director of Planning, Program Analysis, and Evaluation in the Office of Field Operations (OFO) at U.S. Customs and Border Protection (CBP).”
  • “Prior to Pangiam, Andrew (Meehan) served as the principal adviser to the Acting Secretary for external affairs at the Department of Homeland Security (DHS).”
  • “(Tom Plofchan) served as a National Security Advisor to the Department of Energy’s Pacific Northwest National Laboratory before entering government to serve as the Counterterrorism Advisor to the Commissioner, U.S. Customs and Border Protection, and as Counterterrorism Counselor to the Secretary, U.S. Department of Homeland Security.”

So if you thought that veriScan was well-connected because it was offered by an airport authority, consider how well-connected it appears now because it is offered by a company filled with ex-DHS people.

Which in and of itself doesn’t necessarily indicate that the products work, but it does indicate some level of domain knowledge.

But will airports choose to buy the Pangiam veriScan solution…or make their own?

I really want to know (if this song is truly related to crime scene investigation)

I was performing some website maintenance this afternoon, and decided to add a page dedicated to Bredemarket’s services for identity firms. I was trying to think of an introductory illustration to go with the page, since the town crier can only go so far. So, claiming fair use, I decided that this image made perfect sense.

“Who Are You” by The Who. Fair use, https://en.wikipedia.org/w/index.php?curid=11316153

Now while use of the “Who Are You” album cover on a Bredemarket identity page makes perfect sense to me, it may not make sense to 6.9 billion other people. So I guess I should explain my line of thinking.

The link between human identification and the song “Who Are You” was established nearly two decades ago, when the television show “C.S.I. Crime Scene Investigation” started airing on CBS. TV shows have theme songs, and this TV show adopted a (G-rated) excerpt from the Who song “Who Are You” as its theme song. After all, the fictional Las Vegas cops were often tasked with identifying dead bodies or investigating crime scene evidence, so they would be expected to ask the question “who are you” a lot.

Which reminds me of two stories:

  • I actually knew a real Las Vegas crime scene investigator (Rick Workman), but by the time I knew him he was working for the neighboring city of Henderson.
  • CSI spawned a number of spinoffs, including “CSI:Miami.” When I was a Motorola product manager, CSI:Miami contacted us to help with a storyline involving a crime scene palm print. While Motorola software was featured in the episode, the GUI was jazzed up a bit so that it would look good on TV.

So this song (and other Who songs for the CSI spinoffs) is indelibly associated with police crime scene work.

But should it be?

After all, people think that “When a Man Loves a Woman” is a love song based upon its title. But the lyrics show that it’s not a love song at all.

When a man loves a woman
Down deep in his soul
She can bring him such misery
If she is playin’ him for a fool

So are we at fault when we associate Pete Townshend’s 1970s song “Who Are You” with crime scene investigation?

Yes, and no.

While the “who are you” question has nothing to do with figuring out who committed a crime, it DOES involve a policeman.

This song is based on a day in the life of Pete Townshend….

Pete left that bar and passed out in a random doorway in Soho (a part of New York). A policeman recognized him (“A policeman knew my name”) and being kind, woke him and and told him, “You can go sleep at home tonight (instead of a jail cell), if you can get up and walk away.” Pete’s response: “Who the f–k are you?”

Because it was the 1970s, the policeman did not try to identify the drunk Townshend with a mobile fingerprint device linked to a fingerprint identification system, or a camera linked to a facial recognition system.

Instead, the drunk Townshend questioned the authority of the policeman. Which is what you would expect from the guy who wrote the line “I hope I die before I get old.”

Speaking of which, did anybody notice that on the album cover for “Who Are You,” Keith Moon is sitting on a chair that says “Not to Be Taken Away”? Actually, they did…especially since the album was released on August 18, 1978 and Moon died on September 7.

While Moon’s death was investigated, no crime scene investigators were involved.