Those characteristics including ethnicity can be useful for marketing purposes.
I remember reading about a shop that used this sort of technology to profile their customers. It revealed that at a certain time of week they received an unusually large number of Japanese customers (turned out they were on a tour bus route) and responded by repositioning their Japanese member of staff to be a greeter during these periods and it significantly improved their revenue.
A similar product based around this idea might be clothes shop dummies with this built in. This would provide an interesting profile of the people interested in the particular outfit the dummy was modelling which might be useful for further advertising targeting or in the case of a multi-national brand some insights into what they should advertise in which regions.
To compare how well it works compared to OpenCV's face detector: http://www.youtube.com/watch?v=LsK0hzcEyHI
dlib is also great at picking out facial landmarks: http://blog.dlib.net/2014/08/real-time-face-pose-estimation....
Between dlib and OpenBR, I think almost all of these usecases can be covered without commercial APIs - if you have a large enough training set.
Face Recognition is a whole another matter though.