diff --git a/examples/dnn_face_recognition_ex.cpp b/examples/dnn_face_recognition_ex.cpp
index 4ade3862f..051ac13cf 100644
--- a/examples/dnn_face_recognition_ex.cpp
+++ b/examples/dnn_face_recognition_ex.cpp
@@ -40,9 +40,8 @@ using namespace std;
 // The dlib_face_recognition_resnet_model_v1 model used by this example was trained using
 // essentially the code shown in dnn_metric_learning_on_images_ex.cpp except the
 // mini-batches were made larger (35x15 instead of 5x5), the iterations without progress
-// was set to 10000, dlib::jitter_image() was used during training, and the training
-// dataset consisted of about 3 million images instead of 55.  Also, the input layer was
-// locked to images of size 150.
+// was set to 10000, and the training dataset consisted of about 3 million images instead of
+// 55.  Also, the input layer was locked to images of size 150.
 template <template <int,template<typename>class,int,typename> class block, int N, template<typename>class BN, typename SUBNET>
 using residual = add_prev1<block<N,BN,1,tag1<SUBNET>>>;
 
diff --git a/examples/dnn_metric_learning_on_images_ex.cpp b/examples/dnn_metric_learning_on_images_ex.cpp
index 3eda1bdfc..4c3856ac6 100644
--- a/examples/dnn_metric_learning_on_images_ex.cpp
+++ b/examples/dnn_metric_learning_on_images_ex.cpp
@@ -116,7 +116,12 @@ void load_mini_batch (
     // You might want to do some data augmentation at this point.  Here we do some simple
     // color augmentation.
     for (auto&& crop : images)
+    {
         disturb_colors(crop,rnd);
+        // Jitter most crops
+        if (rnd.get_random_double() > 0.1)
+            crop = jitter_image(crop,rnd);
+    }
 
 
     // All the images going into a mini-batch have to be the same size.  And really, all