https://bugs.kde.org/show_bug.cgi?id=498024
--- Comment #25 from Michael Miller <michael_mil...@msn.com> --- (In reply to caulier.gilles from comment #24) > Hi Maik, > > Tom1 and Tom2 will have 2 fingerprints in the database if Tom1 != Tom2. > > Of course if Tom1 == Tom2, what's will be the expected result ? > > Gilles Hi everyone, I tested this scenario when I was first working with YuNet and SFace. Each face is defined by 128 unique floating-point vectors. The only way for Tom1 == Tom2 is if there are 2 exact copies of the same image in the library and image1 is assigned Tom1 and image2 is assigned Tom2. Even a slightly resized version of the same image will result in slightly different values for the face vectors. The classifier selects the face that is the closest match. If there are 2 sets of vectors with exactly the same values, the classifier will select the first one it finds. There won't be an error. An issue I've been researching is sometimes when confirming a face, an extra face is added to the list of confirmed faces. The extra face seems to be random, but is being passed from the UI to the faces engine. I've verified the extra confirmation is not in the faces engine. It happens infrequently, so it wasn't my top priority to research. In a library of over 26,000 confirmed faces, there is a chance this dysfunction has included "extra" faces for a person that shouldn't be there, which would lead to "wrong" faces being presented as suggestions. There's also a good chance that small, pixelated faces have been confirmed, and the face vectors aren't good for matching. Once I'm done with the autotagging rewrite, I'm going to revisit the idea of applying a quality metric to the face thumbnail and excluding low quality faces from training. Cheers, Mike -- You are receiving this mail because: You are watching all bug changes.
