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Davis King 2017-09-10 22:22:55 -04:00
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examples/face_landmark_detection_ex.cpp
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This face detector is made using the classic Histogram of Oriented
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.
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.
Note that the license for the iBUG 300-W dataset excludes commercial use.
So you should contact Imperial College London to find out if it's OK for
you use use this model file in a commercial product.
Also, note that you can train your own models using dlib's machine learning
tools. See train_shape_predictor_ex.cpp to see an example.