https://bugs.kde.org/show_bug.cgi?id=425263
Craig <craigfryer...@gmail.com> changed:
What |Removed |Added
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CC| |craigfryer...@gmail.com
--- Comment #3 from Craig <craigfryer...@gmail.com> ---
I have some suggestions for improvements that encompass this problem.
On a typical set of images the ability to accurately recognise faces declines
as more images are added. The main reason for this is that poor quality images
pollute the reference data that the software uses for the recognition of faces.
Recognised faces that are blurred, out of focus, wearing sunglasses or low
resolution contain information that is too similar to other faces in the pool
of faces to be recognised.
Selective Training Data
One of the best methods to improve the rate of face recognition is to improve
the training data. Instead of having just two categories for faces: Confirmed
and Unconfirmed, an additional category could be added, Reference. In this case
only pictures of a face that are in the Reference category would be used in the
recognition training data. Pictures could be categorised into the Reference
category either manually or automatically. By the manual method good quality
faces across a range of ages and situations (ie wearing glasses) could be
tagged to be used in the Reference training data.
Obviously a large number of reference faces would still be required for the
training data, but in a large album of pictures that won’t be a problem. For
example I have person’s face appearing in over 14,000 images, however many of
these are blurred, out of focus or low resolution. A subset of these images
would still be suitable even if it only contained 10% of the images. In
addition using a smaller training data set would improve the speed of clearing
and rebuilding the training data.
The automatic method could use existing algorithms to exclude “Confirmed” face
pictures that are blurred, low resolution or out of focus from the training
data for that particular face. While an image in totality may not be blurred,
low resolution or out of focus, but the section of the image that includes the
face could be, and thus should be excluded from the training data.
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