I'm working on a project about offline signature verification and I've tried SIFT/SURF algorithms (OpenCV) for comparisson of 2 signature images.
What I've noticed is that when I pass in 2 same pictures I get ~1000 keypoints but when I pass 2 pics of different signatures of same person I get just ~70-80. And when one of the passed pics is a signature of a different person but which has alike style I get ~50-60 keypoints. Some of the points also weren't matching each other at all like they were from 2 different locations.
It's clear to me that these algorithms aren't good for my task but I don't quite understand why.
Could anyone exaplin the reason to me from the maths/algo point of view?
Signature verification is a very difficult task, lots of research efforts have been made but still they are not much accurate in comparing signature pairs
SIFT/SURF algorithms wouldn't be helpful here because model needs to learn a more complex set of features in order to compare signatures
There are some Deep learning based Offline signature verification models that you can see