Comments (5)
For FFHQ, the dlib library is used, so that face alignment is based on 68 landmarks. See: http://dlib.net/face_landmark_detection_ex.cpp.html
This example program shows how to find frontal human faces in an image and
estimate their pose. The pose takes the form of 68 landmarks. These are
points on the face such as the corners of the mouth, along the eyebrows, on
the eyes, and so forth.The face detector we use is made using the classic Histogram of Oriented
Gradients (HOG) feature combined with a linear classifier, an image pyramid,
and sliding window detection scheme. The pose estimator was created by
using dlib's implementation of the paper:
One Millisecond Face Alignment with an Ensemble of Regression Trees by
Vahid Kazemi and Josephine Sullivan, CVPR 2014
and was trained on the iBUG 300-W face landmark dataset (see
https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/):
C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic.
300 faces In-the-wild challenge: Database and results.
Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
You can get the trained model file from:
http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2.
So the use of 5 landmarks is not specific to FFHQ at all. I think I saw it first in RetinaFace:
- https://github.com/biubug6/Pytorch_Retinaface
- https://github.com/deepinsight/insightface/#introduction-2
from facexlib.
Perfect! Thank you so much for answering all my questions!
from facexlib.
These look like coordinates of five points.
See the comment above the line which you quoted:
standard 5 landmarks for FFHQ faces with 512 x 512
facexlib/facexlib/utils/face_restoration_helper.py
Lines 65 to 70 in e5768d1
See the 5 colored dots on this image:
from facexlib.
Thanks Woctezuma! I see it says it's for FFHQ, can I use these with LFW?
I'm still learning... thank you.
from facexlib.
That comment got me worried 🤣
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Related Issues (20)
- increase area to paste.
- can facexlib it support mac m1 architecture
- wrong kernel in guassian blur ? HOT 2
- Feature Request: offload memory usage on storage
- Possible bug in filtering small / angled faces HOT 7
- Cannot install on Python version 3.11.2; only versions >=3.7,<3.11 are supported. HOT 2
- face_resotration_helper.py read_image() cv2 method clobbers 16 bit to 8 bit.
- 4k HD realistic
- bounding_boxes < 0 HOT 1
- Hi! profile faces!! HOT 3
- Error: can't convert cuda:0 device type HOT 1
- Can this handle animal or cartoon animal faces? HOT 1
- Low Quality Faces (blurry) after detecting and cv2.warpalign faces using Facexlib HOT 2
- parsing model half inference not work
- FaceRestoreHelper seems not thread-safe HOT 3
- A general question
- Non existing links in README.md
- onnx
- recognition/__init__.py from 'cuda' to device parameter
- Temporal instability with faces on continuous frames
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from facexlib.