https://bugs.kde.org/show_bug.cgi?id=472266
--- Comment #5 from Justin <[email protected]> --- (In reply to caulier.gilles from comment #4) > Hi Justin, > > We working first to integrate 2 new DNN models (YuNet + SFace) in the face > management to improve performances of the detection and recognition. The > recognition workflow will be changed/fixed for 8.5.0, but it's not yet > complete... > > Questions: > > - Which extra program did you use uner Windows to tag faces in XMP metadata? > - Can you share a file sample to double check? > - Did you uses XMP sidecar file? > > Best > > Gilles Caulier I used Picasa. When I bought a new computer, I began a search for a new photo management software and after trying many different options I found digikam. I believe Picasa has the option to export face region data to xmp. If I still have the old files I will upload a sample. I don't know if the files will be helpful because the face region data may have been edited by the multiple different face tagging software I tried. I have been thinking about face tagging in digikam vs Picasa. Picasa seemed to pre-group similar faces and then assign that grouping to a face tag. Picasa would keep suggesting groups to add to a face tag. It was an efficient way to build up a new database, but I also wonder if this interface hints at how Picasa was handling face detection internally. Digikam's documentation acknowledges that a person's face can vary in appearance for multiple reasons: "One reason can be that there are too many face tags assigned to a person which shows this person in a way that doesn’t really help the search algorithm, e.g. with sunglasses, blurred, unusual colors, carnival make up, dark shaded areas in the face, baby/kid/adult photographs mixed… Another reason to use that option can be false face recognition due to a wrong accuracy value in the Settings tab." What if digikam assigned multiple groups(clusters) to a single person tag? I'm assuming that a detected face is assigned a vector. The Face Accuracy slider determines how similar two vectors must be for the face to be considered the same. Initially the definition of a face results in a tight cluster but as more variations of the same person's face are added the cluster spreads out and may overlap with other people's face clusters. If a person's face is defined by multiple tighter clusters instead of a broad single cluster, the definition of a face wouldn't degrade with increasing number of detected faces assigned to a person. . -- You are receiving this mail because: You are watching all bug changes.
