I’m not sure - I think between the NIST tracks for age estimation and the work entities have done to gather large, diverse sample sets shows meaningful progress and perhaps real world usage.

Your points above are valid and real concerns, in addition to liveliness. There is work further to be done and improvements to be made. But it seems to me that they are solvable problems.

These datasets are getting granular, monolid vs non, 12+ different ethnicity sub groups and so forth.

Do you not think that with enough data it’s solvable?

The system I worked on had (it's larger now) 100M faces in the training set, and when I was leaving there was a 300M set in the works. We went to lengths to collect and categorize. It's mixed ethnicity that throws a wrench into ethnic categorization. It is ordinary to have people with a half dozen or more racial compositions, and that pretty much wreaks categorization. We (they) also had a a pretty robust liveness detection, and surgical mask detection with a "see through the mask" feature too (available with certain crazy-tech cameras.)