Facial Recognition: A Partial List
June 3, 2020
DarkCyber noted “From RealPlayer to Toshiba, Tech Companies Cash in on the Facial Recognition Gold Rush.” The write up provides two interesting things and one idea which is like a truck tire retread.
First, the write up points out that facial recognition or FR is a “gold rush.” That’s a comparison which eluded the DarkCyber research team. There’s no land. No seller of heavy duty pants. No beautiful scenery. No wading in cold water. No hydro mining. Come to think of it, FR is not like a gold rush.
Second, the write up provides a partial list of outfits engaged in facial recognition. The word partial is important. There are some notable omissions, but 45 is an impressive number. That’s the point. Just 45?
The aspect of the write the DarkCyber team ignored is this “from the MBA classroom” observation:
Despite hundreds of vendors currently selling facial recognition technology across the United States, there is no single government body registering the technology’s rollout, nor is there a public-facing list of such companies working with law enforcement. To document which companies are selling such technology today, the best resource the public has is a governmental agency called the National Institute of Standards and Technology.
Governments are doing a wonderful job it seems. Perhaps the European Union should step forward? What about Brazil? China? Russia? The United Nations? With Covid threats apparently declining, maybe the World Health Organization? Yep, governments.
Then, after wanting a central listing of FR vendors, this passage snagged one of my researcher’s attention:
NIST is a government organization responsible for setting scientific measurement standards and testing novel technology. As a public service, NIST also provides a rolling analysis of facial recognition algorithms, which evaluates the accuracy and speed of a vendor’s algorithms. Recently, that analysis has also included aspects of facial recognition field like algorithmic bias based on race, age, and sex. NIST has previously found evidence of bias in a majority of algorithms studied.
Yep, NIST. The group has done an outstanding job for enterprise search. Plus the bias in algorithms has been documented and run through the math grinding wheel for many years. Put in snaps of bad actors and the FR system does indeed learn to match one digital watermark with a similar digital watermark. Run kindergarten snaps through the system and FR matches are essentially useless. Bias? Sure enough.
Consider these ideas:
- An organization, maybe Medium, should build a database of FR companies
- An organization, maybe Medium, should test each of the FR systems using available datasets or better yet building a training set
- An organization, maybe Medium, should set up a separate public policy blog to track government organizations which are not doing the job to Medium’s standards.
There is an interest in facial recognition because there is a need to figure out who is who. There are some civil disturbances underway in a certain high profile country. FR systems may not be perfect, but they may offer a useful tool to some. On the other hand, why not abandon modern tools until they are perfect.
We live in an era of good enough, and that’s what is available.
Stephen E Arnold, June 3, 2020
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