As far as recognition goes, most modern algorithms don't use fiducials (what you call 'nodal points') to do recognition. So there's nothing in our code that has to do with interocular distance or mouth width, etc., so all of the features are "learned". However, fiducials are used to do a frontalization or re-alignment step before passing it through the feature extractor (neural network or otherwise). This type of frontalization step allowed Facebook's deepface algorithm to use locally learned filters for their CNN as opposed to shared weights.[1][2]
[1] http://www.cv-foundation.org/openaccess/content_cvpr_2014/pa...
One of the earlier successes of face recognition as described in Pentland and Moghaddam's paper was to use simple PCA on the images of faces normalized by eye position. This PCA would use all the pixels of a vector made from an image. Obviously there's been a lot of progress since then, but all pixels are getting used for information.
Principal Component Analysis looks interesting, but I'm struggling to understand how 2D pixel data would be transformed into the appropriate covariance matrix. Any insight on the missing link you could offer?
The technology has advanced far beyond that, but that was the start where automatic face recognition first became realistically possible.
If this happens, I suspect it will change human interaction significantly and make it more different to be casually anonymous. I'm not so worried about people I casually interact learning my identity; I'd be more concerned about cradle-to-grave government collection and permanent storage of these records, coupled with license plate scanners, etc.