Improved citations

python_examples/face_landmark_detection.py

@@ -6,19 +6,27 @@
 #   points on the face such as the corners of the mouth, along the eyebrows, on
 #   the eyes, and so forth.
 #
-#   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.
+#   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 in a commercial product.
+#
 #
 #   Also, note that you can train your own models using dlib's machine learning
 #   tools. See train_shape_predictor.py to see an example.
 #
-#   You can get the shape_predictor_68_face_landmarks.dat file from:
-#   http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
 #
 # COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
 #   You can install dlib using the command: