The minisketch library I worked on can be used for near optimal (in the sense of information leak) error correction for "set like" features:<p><a href="https://github.com/sipa/minisketch/" rel="nofollow">https://github.com/sipa/minisketch/</a><p>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.
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.<p>But maybe I am wrong, maybe there are enough bits of information in a randomly lit image of a face.<p>[1] <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2898524/" rel="nofollow">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2898524/</a>