[digiKam-users] Questions about face recognition and workflow

[Will Ramey]

Thank you for sharing these very thoughtful and clearly-phrased questions.

I don't know any of the answers, but if anyone can share insights on even a
few of them it would be helpful to me as well.

There is some helpful information about face tags with rotated images (that
may apply to cropped images as well) in this tutorial:

https://userbase.kde.org/Digikam/Tutorials/Tagging_and_Face_Tags#Rotating_face_tags

About me:
I've recently transitioned our 20+ years collection of about 190,000 images
from Picasa to DigiKam, and am in the process of cleaning up metadata as
well. I am currently using DigiKam on multiple Windows laptops to manage a
shared database and image collection on a NAS.

Best regards,
Will.

On Sun, Oct 16, 2022 at 5:57 AM Jens Benecke <jens-digikam at spamfreemail.de>
wrote:

> Hello,
>
> I am currently cleaning out my photos database of ~150'000 images, taken
> in the last 20 years roughly.
>
> Until 2015, this database was maintained using iPhoto, and I converted
> this to Digikam using a Ruby script (on Github) and a lot of time
> manually correcting stuff. I noticed that some face rectangles were at
> an incorrect position, especially with cropped or rotated images, and a
> lot of labels were simply wrong.
>
> To optimize my future workflow and improve face detection, I would like
> to know a bit more about how Digikams face detection actually works. So
> I have these questions:
>
> 1. How big does a face rectangle need to be if I draw one manually? Does
> it need to contain the whole head of the person, or is "eyes + nose +
> mouth" sufficient? Does this size need to be consistent throughout one
> person or does Digikam not care at all?
>
> 2. How tolerant is Digikam regarding rotated faces, e.g. when people are
> lying, upside down, looking sideways (= profile view), or looking down
> (skewed view)? Does Digikam regard these faces as "different"? If so, is
> this avoidable? I noticed that after importing a lot of images with one
> person lying down, Digikam would suddenly assign this person to ALL
> photos where ANYbody was lying down.
>
> 3. How tolerant is Digikam regarding "decoration" like glasses, scarves,
> half-obscured faces etc? Do such images pollute the learning process?
>
> 4. Does Digikam care about exposure, brightness, or sharpness of images?
> Do these images "hurt" the faces learning process?
> In other words, can I manually tag images where faces are not detected
> and maybe not even clearly visible, but where I *know* the person is
> there, just for my organization, or will this poison the learning database?
>
> 5. How tolearnt is Digikam regarding false positives? If 1 of 100 images
> for a person is incorrect (but confirmed), how much will this interfere
> with the detection of future faces?
>
> I am currently using Digikam 7.9.0 appimage 2022-10-12, on KDE Neon.
>
>
> Thank you!
>
> --
> Regards, Jens
>
>
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