how to optimally use face*tion

Andrey Goreev aegoreev at
Tue Feb 28 23:10:40 GMT 2017

In your case I would started with the default accuracy settings (1/2) and if keep getting plants and unwanted faces would moved the slider to 1/4 and reviewed the results

Sent from my Samsung Galaxy smartphone.
-------- Original message --------From: Boudewijn <wankelwankel at> Date: 2017-02-28  3:48 PM  (GMT-07:00) To: digiKam - Home Manage your photographs as a professional with the power of open source <digikam-users at> Subject: how to optimally use face*tion 
Hi List,

TL;DR: how do I make optimal use of face detection/recognition? I seek to 
improve recognition results.

I use DK, but not as a professional. Mostly family images. I am happy with the 
face detection because it helps to identify and tag pictures with friends and 
family on them quick and easy. 

I think I do not use the most optimal way, so I am hoping for feedback. 

Detecting faces, there are 4 kinds of false hits, and one that I want to tag:
1. detected as face, but it is a plant 
2. detected as a face, but it is a painting of a face
3. detected as a face, but someone in the crowd I don't know
4. detected as a face, but I am not interested in tagging this specific 
instance of the face of this acquaintance
5. detected as a face that I want to tag 

One last category, I'm not sure where to put it: there is a face in the middle 
of the thumbnail, but it is much less zoomed into than usual and got other 
things around it (faces, plants, etc). 

How do I help/disturb the training data most for 
1. detection
2. recognition

1. I would guess that detection is helped by pruning the non-face false hits, 
and keeping faces I am not interested in. I use the upper-right X on the face 
thumbnail to reject it, and the lower-right 'no parking' symbol (circle with 
diagonal slash) to make the thumbnail disappear from the unknown detections.

2. I would guess that recognition is helped by having a small number of 
disperate face tags to choose between, and a large number of confirmed faces 
with each name. 

Lacking time to hang as many tags on each image as I would like, I kept, in 
the end, to a minimum of tagging people (the old fashioned way) and places 
(hardly geo-tagged) as a minimum for making fast intersections between 
combinations of people and visited places (fully realizing those would be the 
first to be automated and making it futile to spend time on those...)

So far face detection helps me, but face recognition does not yield the 
results I hoped for so I end up going through the set of images more than once 
to check for false negative names, false positive names and just for fun.

Maybe I should mention that making the process do anything at all is no 
problem at all after selecting some albums or tags, moving the parameters left 
or right and selecting or deselecting the "All cpu's are belong to DK - all 
cpu's - all cpu's are belong - .. sorry, couldn't leave it ;-) ). 

Thanks for reading through all of this. Thanks even more for everyone involved 
in bringing digiKam!

Best regards,


PS: should the parameter moved to "high accuracy", like 95%, (only) make fewer 
false positive detections, or does it (also) increase false negatives? And 
does it make training data into garbage if I run it in low accuracy once? In 
other words: fast will give me (only) results that are faces for sure after a 
quick scan (skipping difficult faces and fetching plants), and accurate will 
give me "hardly any" plants and "will not" skip difficult faces? 
Will this improve after training, or is each image scrutinized on its own? 
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