![]() On the other hand, face detection sounds like it is working well, so perhaps it should be put to other uses, such as better automatic red-eye reduction, or easier controls for improving skin tone, or selective sharpening, or other such features. They did not seem confident that face recognition would ever be very effective in Digikam. I’m no expert on face recognition, but I have read comments from the developers on the matter. At present, face recognition does not sound like it is ready for the big time and will disappoint far more than it will delight. I think it would need to be well over 50% to avoid being anything other than an annoyance if using it for tagging, though the threshold could be lower if it were used for searching. Is 10% good enough? 90% false positives? I don’t think so. “nn” did not give us a percentage, but I would take “very low” to mean less than 10% (please correct me, “nn”). ![]() However, I think this comment by “nn”, “Well, it works somewhat but the detection rate is very low,” suggests that we are dealing with a rather dark shade of grey. Of course the world is not, as you write, black and white I did not claim that that face recognition had to be 100% perfect before it could be released. Wait until it is useful and then wow us with it. In its current immature state, it will only drag down the perceived quality of the whole product for little real gain. Digikam 2.0 will probably be better off without a very ineffective implementation of many users’ most wished-for feature. I’m just trying to point out that what is a fine and laudable achievement for the developers will, at this early stage, just be a bitter disappointment to many expectant users. I’m sorry if I sound like I’m being negative. The answer is that it will handle ageing just as well as any other recognition challenge, it will fail quite miserably. Is that about right?Ĭommenters are wondering how well it will handle ageing. So, what we’ll have is a system that is very good at pointing out to us that there are faces in the photograph that we are looking at, but if we let it tag those faces it will identify nearly all of those faces incorrectly and we’ll have to start over again and do it manually. You can then go through the scanned photos to fix face tags and remove incorrectly identified images. Once the scan is completed, you should see all photos containing faces. Press then the Scan button and let digiKam do its job. While at it, you can tweak the face detection parameters in the Parameters section. To do this, press the Options button and select the albums and tags you want from the Search in drop-down list in the Albums section. ![]() By default, digiKam scans all collections and tags, but you can limit the scan operation to certain albums and tags. In the Scanning Faces window tick the Detect and recognize faces check box. Expand the People sidebar, and press the Scan collections for faces button. Fortunately, digiKam can do the donkey job of automatically identifying faces for you. Tagging faces manually can be a daunting proposition, especially if you have a considerable number of photos of people. Open the photo you want in the preview pane, press the Add a Face Tag button, draw a rectangle around a face on the photo, enter the face tag (e.g., the person’s name), and press Confirm. Tagging faces in digiKam is a rather straightforward procedure. This lets you quickly locate all photos of a specific person using digiKam filtering capabilities. Face recognition has been one of the most requested digiKam features, and the latest version of the photo management application provides this functionality.Īs the name suggests, the face recognition functionality can be used to find photos containing faces and attach face tags to persons in photos. ![]()
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