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diff --git a/ml/dlib/examples/face_detection_ex.cpp b/ml/dlib/examples/face_detection_ex.cpp
deleted file mode 100644
index 9569d44e9..000000000
--- a/ml/dlib/examples/face_detection_ex.cpp
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-// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
-/*
-
-    This example program shows how to find frontal human faces in an image.  In
-    particular, this program shows how you can take a list of images from the
-    command line and display each on the screen with red boxes overlaid on each
-    human face.
-
-    The examples/faces folder contains some jpg images of people.  You can run
-    this program on them and see the detections by executing the following command:
-        ./face_detection_ex faces/*.jpg
-
-    
-    This face detector is made using the now classic Histogram of Oriented
-    Gradients (HOG) feature combined with a linear classifier, an image pyramid,
-    and sliding window detection scheme.  This type of object detector is fairly
-    general and capable of detecting many types of semi-rigid objects in
-    addition to human faces.  Therefore, if you are interested in making your
-    own object detectors then read the fhog_object_detector_ex.cpp example
-    program.  It shows how to use the machine learning tools which were used to
-    create dlib's face detector. 
-
-
-    Finally, note that the face detector is fastest when compiled with at least
-    SSE2 instructions enabled.  So if you are using a PC with an Intel or AMD
-    chip then you should enable at least SSE2 instructions.  If you are using
-    cmake to compile this program you can enable them by using one of the
-    following commands when you create the build project:
-        cmake path_to_dlib_root/examples -DUSE_SSE2_INSTRUCTIONS=ON
-        cmake path_to_dlib_root/examples -DUSE_SSE4_INSTRUCTIONS=ON
-        cmake path_to_dlib_root/examples -DUSE_AVX_INSTRUCTIONS=ON
-    This will set the appropriate compiler options for GCC, clang, Visual
-    Studio, or the Intel compiler.  If you are using another compiler then you
-    need to consult your compiler's manual to determine how to enable these
-    instructions.  Note that AVX is the fastest but requires a CPU from at least
-    2011.  SSE4 is the next fastest and is supported by most current machines.  
-*/
-
-
-#include <dlib/image_processing/frontal_face_detector.h>
-#include <dlib/gui_widgets.h>
-#include <dlib/image_io.h>
-#include <iostream>
-
-using namespace dlib;
-using namespace std;
-
-// ----------------------------------------------------------------------------------------
-
-int main(int argc, char** argv)
-{  
-    try
-    {
-        if (argc == 1)
-        {
-            cout << "Give some image files as arguments to this program." << endl;
-            return 0;
-        }
-
-        frontal_face_detector detector = get_frontal_face_detector();
-        image_window win;
-
-        // Loop over all the images provided on the command line.
-        for (int i = 1; i < argc; ++i)
-        {
-            cout << "processing image " << argv[i] << endl;
-            array2d<unsigned char> img;
-            load_image(img, argv[i]);
-            // Make the image bigger by a factor of two.  This is useful since
-            // the face detector looks for faces that are about 80 by 80 pixels
-            // or larger.  Therefore, if you want to find faces that are smaller
-            // than that then you need to upsample the image as we do here by
-            // calling pyramid_up().  So this will allow it to detect faces that
-            // are at least 40 by 40 pixels in size.  We could call pyramid_up()
-            // again to find even smaller faces, but note that every time we
-            // upsample the image we make the detector run slower since it must
-            // process a larger image.
-            pyramid_up(img);
-
-            // Now tell the face detector to give us a list of bounding boxes
-            // around all the faces it can find in the image.
-            std::vector<rectangle> dets = detector(img);
-
-            cout << "Number of faces detected: " << dets.size() << endl;
-            // Now we show the image on the screen and the face detections as
-            // red overlay boxes.
-            win.clear_overlay();
-            win.set_image(img);
-            win.add_overlay(dets, rgb_pixel(255,0,0));
-
-            cout << "Hit enter to process the next image..." << endl;
-            cin.get();
-        }
-    }
-    catch (exception& e)
-    {
-        cout << "\nexception thrown!" << endl;
-        cout << e.what() << endl;
-    }
-}
-
-// ----------------------------------------------------------------------------------------
-