diff --git a/examples/face_detection_ex.cpp b/examples/face_detection_ex.cpp
index 4213b451f..00e4c555d 100644
--- a/examples/face_detection_ex.cpp
+++ b/examples/face_detection_ex.cpp
@@ -1,7 +1,39 @@
 // 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:
+        ./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 used to create this
+    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.  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_dclib/examples -DUSE_SSE2_INSTRUCTIONS=ON
+        cmake path_to_dclib/examples -DUSE_SSE4_INSTRUCTIONS=ON
+        cmake path_to_dclib/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.  
 */
 
 
@@ -19,20 +51,44 @@ 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;
+            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));
-            // Pause until the user hits the enter key
+
+            cout << "Hit enter to process the next image..." << endl;
             cin.get();
         }
     }