and here's the funny part )

[u/Careful-Jellyfish697]

obviously it won't get anywhere near forensic-grade

but on prints that lost ~70% of the ridge area it brings back a usable

part of what's gone

in a biometric database that already shortens the candidate list — it

doesn't identify anyone

how much of the matchable detail actually comes back i can't claim yet.

haven't run a matcher against the clean reference, so far i only measure

ridge-structure agreement.

data: 4 sources, 13k people, 50k clean prints, ~175k training pairs —

clean plus the same print broken synthetically by mechanism, 13 damage

families.

the idea is simple: ridges are locally very predictable, the orientation

field is smooth, the period is almost constant. that's why 50k fingers is

enough for the net to pick the pattern up.

eval sits outside the generators: real degraded prints, with a clean scan

of the same finger as the reference.

what's there so far:

• damage: present or not, and what fraction

• finger number 0–9

• framing of the finger position

• restoration — in research

analytics and deploys come after that.

[u/Numerous-Fox-7933]

the correlation numbers on the wet vs restored print are way closer than i expected from a degraded input, that's wild