472645 2023-07-26 05:26:45 +0000 Face Recognition does not recognize any faces even after rebuilding and retraining all training data. 2025-03-15 13:27:05 +0000 1 1 2 Applications digikam Faces-Recognition 8.2.0 Microsoft Windows Microsoft Windows RESOLVED FIXED NOR normal --- 0 Stewart.Bodzin digikam-bugs-null caulier.gilles metzpinguin pschonmann 8.7.0 0 oldest_to_newest 2242804 0 Stewart.Bodzin 2023-07-26 05:26:45 +0000 Face recognition is really broken. Upgraded to 8.2.0 and no faces seem to get correctly recognized. I cleaned up any mis-identified faces and also did a full Finger-prints rebuild and did a Detect and recognize faces from the maintenance menu. Selected clear and rebuild all training data. Face detection continues to run ok but the face recognition in all modes with and without YOLO is totally inaccurate on all faces. I have many faces identified for each unique person and did the rebuild training. However, even on a single image, selecting the context menu to recognize faces finds no faces on all photos I have tested. Common faces are also not found on any single photo. Something is seriously wrong. Thank you. 2242809 1 metzpinguin 2023-07-26 05:51:00 +0000 Have you narrowed down the face detection/recognition to albums or tags in the People sidebar in the settings. Unfortunately, users wanted us to turn face recognition back to select albums as well. Select all albums and no tags. Maik 2242819 2 metzpinguin 2023-07-26 07:47:48 +0000 The problem is, the more confirmed faces you have from one person, the more watered down the result and you end up not recognizing faces. We have to think about limiting it. Maik *** This bug has been marked as a duplicate of bug 431797 *** 2243044 3 160558 Stewart.Bodzin 2023-07-27 05:45:25 +0000 Created attachment 160558 attachment-3958260-0.html Hey Maik. I agree with your musings about this problem. The process should have two types of confirm. 1. Confirm the face is correctly identified or 2. Confirm the face is correctly identified and commit that face image to the training data set for the neural net. This way we'd only need to identify a handful of good face identifiable pictures during the beginning of a new face and then do confirms just to tag the picture but not to pollute the training dataset.Just my 2 cents.Sent from my Galaxy -------- Original message --------From: Maik Qualmann <bugzilla_noreply@kde.org> Date: 7/26/23 1:39 AM (GMT-08:00) To: Stewart.Bodzin@usa.net Subject: [digikam] [Bug 472645] Face Recognition does not recognize any faces even after rebuilding and retraining all training data. https://bugs.kde.org/show_bug.cgi?id=472645Maik Qualmann <metzpinguin@gmail.com> changed:           What    |Removed                     |Added----------------------------------------------------------------------------         Resolution|---                         |DUPLICATE             Status|REPORTED                    |RESOLVED--- Comment #2 from Maik Qualmann <metzpinguin@gmail.com> ---The problem is, the more confirmed faces you have from one person, the morewatered down the result and you end up not recognizing faces. We have to thinkabout limiting it.Maik*** This bug has been marked as a duplicate of bug 431797 ***-- You are receiving this mail because:You reported the bug. 2407855 4 caulier.gilles 2025-03-15 13:27:05 +0000 Fixed with 431797 160558 2023-07-27 05:45:25 +0000 2023-07-27 05:45:25 +0000 attachment-3958260-0.html attachment-3958260-0.html text/html 2566 Stewart.Bodzin PGh0bWw+PGhlYWQ+PG1ldGEgaHR0cC1lcXVpdj0iQ29udGVudC1UeXBlIiBjb250ZW50PSJ0ZXh0 L2h0bWw7IGNoYXJzZXQ9VVRGLTgiPjwvaGVhZD48Ym9keSBkaXI9ImF1dG8iPjxkaXYgZGlyPSJh dXRvIj5IZXkgTWFpay4gSSBhZ3JlZSB3aXRoIHlvdXIgbXVzaW5ncyBhYm91dCB0aGlzIHByb2Js ZW0uIFRoZSBwcm9jZXNzIHNob3VsZCBoYXZlIHR3byB0eXBlcyBvZiBjb25maXJtLiAxLiBDb25m aXJtIHRoZSBmYWNlIGlzIGNvcnJlY3RseSBpZGVudGlmaWVkIG9yIDIuIENvbmZpcm0gdGhlIGZh Y2UgaXMgY29ycmVjdGx5IGlkZW50aWZpZWQgYW5kIGNvbW1pdCB0aGF0IGZhY2UgaW1hZ2UgdG8g dGhlIHRyYWluaW5nIGRhdGEgc2V0IGZvciB0aGUgbmV1cmFsIG5ldC4gVGhpcyB3YXkgd2UnZCBv bmx5IG5lZWQgdG8gaWRlbnRpZnkgYSBoYW5kZnVsIG9mIGdvb2QgZmFjZSBpZGVudGlmaWFibGUg cGljdHVyZXMgZHVyaW5nIHRoZSBiZWdpbm5pbmcgb2YgYSBuZXcgZmFjZSBhbmQgdGhlbiBkbyBj b25maXJtcyBqdXN0IHRvIHRhZyB0aGUgcGljdHVyZSBidXQgbm90IHRvIHBvbGx1dGUgdGhlIHRy YWluaW5nIGRhdGFzZXQuSnVzdCBteSA8YT4yIGNlbnRzLjwvYT48L2Rpdj48ZGl2IGRpcj0iYXV0 byI+PGJyPjwvZGl2PjxkaXYgZGlyPSJhdXRvIj48YnI+PC9kaXY+PGRpdiBkaXI9ImF1dG8iPjxi cj48L2Rpdj48ZGl2IGlkPSJjb21wb3Nlcl9zaWduYXR1cmUiIGRpcj0iYXV0byI+PGRpdiBzdHls ZT0iZm9udC1zaXplOjg1JTtjb2xvcjojNTc1NzU3IiBkaXI9ImF1dG8iPlNlbnQgZnJvbSBteSBH YWxheHk8L2Rpdj48L2Rpdj48ZGl2IGRpcj0iYXV0byI+PGJyPjwvZGl2PjxkaXY+PGJyPjwvZGl2 PjxkaXYgYWxpZ249ImxlZnQiIGRpcj0iYXV0byIgc3R5bGU9ImZvbnQtc2l6ZToxMDAlO2NvbG9y OiMwMDAwMDAiPjxkaXY+LS0tLS0tLS0gT3JpZ2luYWwgbWVzc2FnZSAtLS0tLS0tLTwvZGl2Pjxk aXY+RnJvbTogTWFpayBRdWFsbWFubiAmbHQ7YnVnemlsbGFfbm9yZXBseUBrZGUub3JnJmd0OyA8 L2Rpdj48ZGl2PkRhdGU6IDcvMjYvMjMgIDE6MzkgQU0gIChHTVQtMDg6MDApIDwvZGl2PjxkaXY+ VG86IFN0ZXdhcnQuQm9kemluQHVzYS5uZXQgPC9kaXY+PGRpdj5TdWJqZWN0OiBbZGlnaWthbV0g W0J1ZyA0NzI2NDVdIEZhY2UgUmVjb2duaXRpb24gZG9lcyBub3QgcmVjb2duaXplIGFueSBmYWNl cyBldmVuCiAgYWZ0ZXIgcmVidWlsZGluZyBhbmQgcmV0cmFpbmluZyAgYWxsIHRyYWluaW5nIGRh dGEuIDwvZGl2PjxkaXY+PGJyPjwvZGl2PjwvZGl2Pmh0dHBzOi8vYnVncy5rZGUub3JnL3Nob3df YnVnLmNnaT9pZD00NzI2NDU8YnI+PGJyPk1haWsgUXVhbG1hbm4gJmx0O21ldHpwaW5ndWluQGdt YWlsLmNvbSZndDsgY2hhbmdlZDo8YnI+PGJyPiZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyBXaGF0Jm5ic3A7Jm5ic3A7Jm5ic3A7IHxS ZW1vdmVkJm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5i c3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7Jm5ic3A7 Jm5ic3A7Jm5ic3A7IHxBZGRlZDxicj4tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0t LS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tLS0tPGJyPiZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyBSZXNvbHV0aW9ufC0tLSZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZu YnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyB8RFVQTElDQVRFPGJyPiZuYnNwOyZuYnNwOyZuYnNw OyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyZuYnNwOyBT dGF0dXN8UkVQT1JURUQmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsm bmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJzcDsmbmJz cDsmbmJzcDsmbmJzcDsgfFJFU09MVkVEPGJyPjxicj4tLS0gQ29tbWVudCAjMiBmcm9tIE1haWsg UXVhbG1hbm4gJmx0O21ldHpwaW5ndWluQGdtYWlsLmNvbSZndDsgLS0tPGJyPlRoZSBwcm9ibGVt IGlzLCB0aGUgbW9yZSBjb25maXJtZWQgZmFjZXMgeW91IGhhdmUgZnJvbSBvbmUgcGVyc29uLCB0 aGUgbW9yZTxicj53YXRlcmVkIGRvd24gdGhlIHJlc3VsdCBhbmQgeW91IGVuZCB1cCBub3QgcmVj b2duaXppbmcgZmFjZXMuIFdlIGhhdmUgdG8gdGhpbms8YnI+YWJvdXQgbGltaXRpbmcgaXQuPGJy Pjxicj5NYWlrPGJyPjxicj4qKiogVGhpcyBidWcgaGFzIGJlZW4gbWFya2VkIGFzIGEgZHVwbGlj YXRlIG9mIGJ1ZyA0MzE3OTcgKioqPGJyPjxicj4tLSA8YnI+WW91IGFyZSByZWNlaXZpbmcgdGhp cyBtYWlsIGJlY2F1c2U6PGJyPllvdSByZXBvcnRlZCB0aGUgYnVnLjxicj48L2JvZHk+PC9odG1s Pg==