diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 8338721d1..f8fbe18aa 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -94,5 +94,6 @@ add_example(thread_pool_ex)
add_example(threads_ex)
add_example(timer_ex)
add_example(train_object_detector)
+add_example(train_shape_predictor_ex)
add_example(using_custom_kernels_ex)
add_example(xml_parser_ex)
diff --git a/examples/faces/testing_with_face_landmarks.xml b/examples/faces/testing_with_face_landmarks.xml
new file mode 100644
index 000000000..7589561b1
--- /dev/null
+++ b/examples/faces/testing_with_face_landmarks.xml
@@ -0,0 +1,1772 @@
+
+
+
+Testing faces
+These are images from the PASCAL VOC 2011 dataset.
+ The face landmarks are from dlib's shape_predictor_68_face_landmarks.dat
+ landmarking model. The model uses the 68 landmark scheme used by the iBUG
+ 300-W dataset.
+
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diff --git a/examples/faces/training_with_face_landmarks.xml b/examples/faces/training_with_face_landmarks.xml
new file mode 100644
index 000000000..b87e75350
--- /dev/null
+++ b/examples/faces/training_with_face_landmarks.xml
@@ -0,0 +1,1280 @@
+
+
+
+Training faces
+These are images from the PASCAL VOC 2011 dataset.
+ The face landmarks are from dlib's shape_predictor_68_face_landmarks.dat
+ landmarking model. The model uses the 68 landmark scheme used by the iBUG
+ 300-W dataset.
+
+
+
+
+
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diff --git a/examples/train_shape_predictor_ex.cpp b/examples/train_shape_predictor_ex.cpp
new file mode 100644
index 000000000..557e82c43
--- /dev/null
+++ b/examples/train_shape_predictor_ex.cpp
@@ -0,0 +1,139 @@
+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+
+
+
+ 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
+
+*/
+
+
+#include
+#include
+#include
+
+using namespace dlib;
+using namespace std;
+
+// ----------------------------------------------------------------------------------------
+
+std::vector > get_interocular_distances (
+ const std::vector >& objects
+);
+
+// ----------------------------------------------------------------------------------------
+
+int main(int argc, char** argv)
+{
+ try
+ {
+ // In this example we are going to train a shape_predictor based on the
+ // small faces dataset in the examples/faces directory. So the first
+ // thing we do is load that dataset. This means you need to supply the
+ // path to this faces folder as a command line argument so we will know
+ // where it is.
+ if (argc != 2)
+ {
+ cout << "Give the path to the examples/faces directory as the argument to this" << endl;
+ cout << "program. For example, if you are in the examples folder then execute " << endl;
+ cout << "this program by running: " << endl;
+ cout << " ./train_shape_predictor_ex faces" << endl;
+ cout << endl;
+ return 0;
+ }
+ const std::string faces_directory = argv[1];
+ // The faces directory contains a training dataset and a separate
+ // testing dataset. The training data consists of 4 images, each
+ // annotated with rectangles that bound each human face along with 68
+ // face landmarks on each face. The idea is to use this training data
+ // to learn to identify the position of landmarks on human faces in new
+ // images.
+ //
+ // Once you have trained a shape_predictor it is always important to
+ // test it on data it wasn't trained on. Therefore, we will also load
+ // a separate testing set of 5 images. Once we have a shape_predictor
+ // created from the training data we will see how well it works by
+ // running it on the testing images.
+ //
+ // So here we create the variables that will hold our dataset.
+ // images_train will hold the 4 training images and face_boxes_train
+ // holds the locations of the faces in the training images. So for
+ // example, the image images_train[0] has the faces given by the
+ // full_object_detections in face_boxes_train[0].
+ dlib::array > images_train, images_test;
+ std::vector > faces_train, faces_test;
+
+ // Now we load the data. These XML files list the images in each
+ // dataset and also contain the positions of the face boxes and landmark
+ // (called parts in the XML file). Obviously you can use any kind of
+ // input format you like so long as you store the data into images_train
+ // and faces_train.
+ load_image_dataset(images_train, faces_train, faces_directory+"/training_with_face_landmarks.xml");
+ load_image_dataset(images_test, faces_test, faces_directory+"/testing_with_face_landmarks.xml");
+
+ shape_predictor_trainer trainer;
+ shape_predictor sp = trainer.train(images_train, faces_train);
+
+
+ cout << "mean training error: "<< test_shape_predictor(sp, images_train, faces_train, get_interocular_distances(faces_train)) << endl;
+ cout << "mean testing error: "<< test_shape_predictor(sp, images_test, faces_test, get_interocular_distances(faces_test)) << endl;
+
+ serialize("sp.dat") << sp;
+ }
+ catch (exception& e)
+ {
+ cout << "\nexception thrown!" << endl;
+ cout << e.what() << endl;
+ }
+}
+
+// ----------------------------------------------------------------------------------------
+
+double interocular_distance (
+ const full_object_detection& det
+)
+{
+ dlib::vector l, r;
+ double cnt = 0;
+ // Find the center of the left eye by averaging the points around
+ // the eye.
+ for (unsigned long i = 36; i <= 41; ++i)
+ {
+ l += det.part(i);
+ ++cnt;
+ }
+ l /= cnt;
+
+ // Find the center of the right eye by averaging the points around
+ // the eye.
+ cnt = 0;
+ for (unsigned long i = 42; i <= 47; ++i)
+ {
+ r += det.part(i);
+ ++cnt;
+ }
+ r /= cnt;
+
+ // Now return the distance between the centers of the eyes
+ return length(l-r);
+}
+
+std::vector > get_interocular_distances (
+ const std::vector >& objects
+)
+{
+ std::vector > temp(objects.size());
+ for (unsigned long i = 0; i < objects.size(); ++i)
+ {
+ for (unsigned long j = 0; j < objects[i].size(); ++j)
+ {
+ temp[i].push_back(interocular_distance(objects[i][j]));
+ }
+ }
+ return temp;
+}
+
+// ----------------------------------------------------------------------------------------
+