Comments (3)
The photos I used are from
http://xdesktopwallpapers.com/wp-content/uploads/2012/04/Zooey%20Deschanel%20Sad%20Side%20Face%20Closeup.jpg
http://74211.com/wallpaper/picture_big/Anne-Hathaway-HD-Post-in-Pixel-of-1920x1440-in-Smiling-Facial-Expression-and-Messy-Hair-She-is-Impressive-and-Fit-TV-Movies-Post.jpg
and
http://mag-cosmo-prod-s3.s3.amazonaws.com/legacy/images/beauty/neck.jpg
from clandmark.
Hi @cat3333,
I think the main problem is that those faces have too big inplane rotation (roll angle), which makes them difficult to be detected by a conventional face detector such as OpenCV haarcascade
used in the examples.
CLandmark
expects detected face box on its input, so you can use any technique you want to improve the results. Here are the examples using a commercial face detector (implementing this paper), which is able to detect faces in a full yaw range and inplane rotation up to 90 degrees:
So, to answer your questions:
- It is indeed possible to detect close to profile faces (even with a strong inplane rotation), you can use
haarcascade
for profile faces (and try to rotate the image in order to deal with the inplane rotations). - The
modelDir
in the code snippet should contain the folder with multi-view model files (xml
files), which are listed in themodelList
array.
from clandmark.
Thank you very much for the detailed solution!!!
from clandmark.
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