The minisketch library I worked on can be used for near optimal (in the sense of information leak) error correction for "set like" features:

https://github.com/sipa/minisketch/

Our application is for communications efficient set reconciliation to convert Bitcoin's quadratic-overhead transaction gossip protocol (O(txn*peers)) to effectively linear (O(txn)), though the primary academic work that our work was based on were concerned with fuzzy extractors for privacy preserving (and encryption key generating) biometrics.

For more info on fuzzy extractors from biometrics see also this great paper (and the linked survey): http://www.cs.bu.edu/~reyzin/fuzzy.html

Hmm...

This is old research, which seems to be a recreation of the work of Sutcu et al. among others.

I did my masters thesis on this.

Link?

I feel like the ability for this method to work well depends on the methodology of taking the enrollment and the subsequent key-generation images. If you take them using the same poses, with the same camera and lighting within a few hours of each other then this method will work extremely well [1]. I really doubt it generalizes to the case of using it with a laptop webcam in any location with different lighting.

But maybe I am wrong, maybe there are enough bits of information in a randomly lit image of a face.

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2898524/

Has IBM built a product around this? I don’t know of one.

Or is the research for patent purposes only?

Someone at the University of Haifa has a sense of humor