Improved citations

examples/face_landmark_detection_ex.cpp

@@ -8,13 +8,23 @@
     
 
 
-    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.