As I’ve noted before, there are a number of facial recognition companies that claim to be the #1 NIST facial recognition vendor. I’m here to help you cut through the clutter so you know who the #1 NIST facial recognition vendor truly is.
You can confirm this information yourself by visiting the NIST FRVT 1:1 Verification and FRVT 1:N Identification pages. FRVT, by the way, stands for “Face Recognition Vendor Test.”
So I can announce to you that as of February 23, 2022, the #1 NIST facial recognition vendor is Cloudwalk.
And Beihang University ERCACAT.
And Chosun University.
And iSAP Solution Corporation.
And Visage Techologies.
And Expasoft LLC.
And Rank One.
Now how can ALL dozen-plus of these entities be number 1?
The NIST 1:1 and 1:N tests include many different accuracy and performance measurements, and each of the entities listed above placed #1 in at least one of these measurements. And all of the databases, database sizes, and use cases measure very different things.
- Visage Technologies was #1 in the 1:1 performance measurements for template generation time, in milliseconds, for 480×720 and 960×1440 data.
- Meanwhile, NEC was #1 in the 1:N Identification (T>0) accuracy measurements for gallery border, probe border with a delta T greater than or equal to 10 years, N = 1.6 million.
- Not to be confused with the 1:N Identification (T>0) accuracy measurements for gallery visa, probe border, N = 1.6 million, where the #1 algorithm was not from NEC.
- And not to be confused with the 1:N Investigation (R = 1, T = 0) accuracy measurements for gallery border, probe border with a delta T greater than or equal to 10 years, N = 1.6 million, where the #1 algorithm was not from NEC.
And can I add a few more caveats?
First caveat: Since all of these tests are ongoing tests, you can probably find a slightly different set of #1 algorithms if you look at the January data, and you will probably find a slightly different set of #1 algorithms when the March data is available.
Second caveat: These are the results for the unqualified #1 NIST categories. You can add qualifiers, such as “#1 non-Chinese vendor” or “#1 western vendor” or “#1 U.S. vendor” to vault a particular algorithm to the top of the list.
Third caveat: You can add even more qualifiers, such as “within the top five NIST vendors” and (one I admit to having used before) “a top tier NIST vendor in multiple categories.” This can mean whatever you want it to mean. (As can “dramatically improved” algorithm, which may mean that you vaulted from position #300 to position #200 in one of the categories.)
Fourth caveat: Even if a particular NIST test applies to your specific use case, #1 performance on a NIST test does not guarantee that a facial recognition system supplied by that entity will yield #1 performance with your database in your environment. The algorithm sent to NIST may or may not make it into a production system. And even if it does, performance against a particular NIST test database may not yield the same results as performance against a Rhode Island criminal database, a French driver’s license database, or a Nigerian passport database. For more information on this, see Mike French’s LinkedIn article “Why agencies should conduct their own AFIS benchmarks rather than relying on others.”
So now that you know who the #1 NIST facial recognition vendor is, do you feel more knowledgeable?
Although I’ll grant that a NIST accuracy or performance claim is better than some other claims, such as self-test results.