https://bugs.kde.org/show_bug.cgi?id=497491
--- Comment #7 from t...@basien.de ---
Dear Mike and Maik,
I’m not sure if my previous request was fully understood, so I’d like to
clarify my idea with a more algorithmic approach.
The current Digikam database contains information about regions with confirmed
people in images, independent of EXIF data. My goal is to replace the existing,
imprecise face recognition model with a new one, built on a better and more
extensive data foundation.
Here’s my proposed approach:
1. **Resetting Unconfirmed Data:**
All unconfirmed or unknown people are removed from the model. Only confirmed
people and their corresponding regions remain in the database.
2. **Training a New Model:**
The face recognition model is retrained from scratch, using only images
where people have been 100% confirmed.
- **Step-by-Step Process:**
a. Iterate through all confirmed people in the database.
b. For each person, use all associated images and marked face regions to
train the model.
3. **Result:**
A new, improved face recognition model is created, based on the currently
confirmed people.
4. **Next Steps:**
As intended in the product, this new model can then be used to detect
additional people across the photo collection.
This feature would enable more precise face recognition, especially for users
who have already manually tagged a significant portion of their collection. It
also offers an effective way to fix existing inaccuracies by rebuilding the
model with personalized and verified data.
Thank you for considering my suggestion, and for your ongoing work on this
excellent tool!
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