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diff --git a/ml/dlib/examples/face_detection_ex.cpp b/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;
+    }
+}
+
+// ----------------------------------------------------------------------------------------
+