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.
But maybe I am wrong, maybe there are enough bits of information in a randomly lit image of a face.
7 comments
[ 0.23 ms ] story [ 35.5 ms ] threadOr is the research for patent purposes only?
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.
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.
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/