diff --git a/samples/DeepLearningFaceDetection.java b/samples/DeepLearningFaceDetection.java
new file mode 100644
index 00000000..13443623
--- /dev/null
+++ b/samples/DeepLearningFaceDetection.java
@@ -0,0 +1,112 @@
+import org.bytedeco.javacpp.indexer.FloatIndexer;
+import static org.bytedeco.javacpp.opencv_core.CV_32F;
+import org.bytedeco.javacpp.opencv_core.Mat;
+import org.bytedeco.javacpp.opencv_core.Point;
+import org.bytedeco.javacpp.opencv_core.Rect;
+import org.bytedeco.javacpp.opencv_core.Scalar;
+import org.bytedeco.javacpp.opencv_core.Size;
+import org.bytedeco.javacpp.opencv_dnn.Net;
+import static org.bytedeco.javacpp.opencv_dnn.blobFromImage;
+import static org.bytedeco.javacpp.opencv_dnn.readNetFromCaffe;
+import static org.bytedeco.javacpp.opencv_imgproc.rectangle;
+import static org.bytedeco.javacpp.opencv_imgproc.resize;
+import static org.bytedeco.javacpp.opencv_videoio.CV_CAP_PROP_FRAME_HEIGHT;
+import static org.bytedeco.javacpp.opencv_videoio.CV_CAP_PROP_FRAME_WIDTH;
+import org.bytedeco.javacpp.opencv_videoio.VideoCapture;
+import org.bytedeco.javacv.CanvasFrame;
+import org.bytedeco.javacv.OpenCVFrameConverter;
+
+/**
+ * Created on Jul 28, 2018 
+ *
+ * @author Taha Emara 
+ * Email : taha@emaraic.com
+ *
+ * This example does face detection using deep learning model which provides a
+ * great accuracy compared to OpenCV face detection using Haar cascades.
+ *
+ * This example is based on this code
+ * https://github.com/opencv/opencv/blob/master/modules/dnn/misc/face_detector_accuracy.py
+ *
+ * To run this example you need two files: deploy.prototxt can be downloaded
+ * from
+ * https://github.com/opencv/opencv/blob/master/samples/dnn/face_detector/deploy.prototxt
+ *
+ * and res10_300x300_ssd_iter_140000.caffemodel
+ * https://github.com/opencv/opencv_3rdparty/blob/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel
+ *
+ */
+public class DeepLearningFaceDetection {
+
+    private static final String PROTO_FILE = "deploy.prototxt";
+    private static final String CAFFE_MODEL_FILE = "res10_300x300_ssd_iter_140000.caffemodel";
+    private static final OpenCVFrameConverter.ToIplImage converter = new OpenCVFrameConverter.ToIplImage();
+    private static Net net = null;
+
+    static {
+        net = readNetFromCaffe(PROTO_FILE, CAFFE_MODEL_FILE);
+    }
+
+    private static void detectAndDraw(Mat image) {//detect faces and draw a blue rectangle arroung each face
+
+        resize(image, image, new Size(300, 300));//resize the image to match the input size of the model
+
+        //create a 4-dimensional blob from image with NCHW (Number of images in the batch -for training only-, Channel, Height, Width) dimensions order,
+        //for more detailes read the official docs at https://docs.opencv.org/trunk/d6/d0f/group__dnn.html#gabd0e76da3c6ad15c08b01ef21ad55dd8
+        Mat blob = blobFromImage(image, 1.0, new Size(300, 300), new Scalar(104.0, 177.0, 123.0, 0), false, false);
+
+        net.setInput(blob);//set the input to network model
+        Mat output = net.forward();//feed forward the input to the netwrok to get the output matrix
+
+        Mat ne = new Mat(new Size(output.size(3), output.size(2)), CV_32F, output.ptr(0, 0));//extract a 2d matrix for 4d output matrix with form of (number of detections x 7)
+
+        FloatIndexer srcIndexer = ne.createIndexer(); // create indexer to access elements of the matric
+
+        for (int i = 0; i < output.size(3); i++) {//iterate to extract elements
+            float confidence = srcIndexer.get(i, 2);
+            float f1 = srcIndexer.get(i, 3);
+            float f2 = srcIndexer.get(i, 4);
+            float f3 = srcIndexer.get(i, 5);
+            float f4 = srcIndexer.get(i, 6);
+            if (confidence > .6) {
+                float tx = f1 * 300;//top left point's x
+                float ty = f2 * 300;//top left point's y
+                float bx = f3 * 300;//bottom right point's x
+                float by = f4 * 300;//bottom right point's y
+                rectangle(image, new Rect(new Point((int) tx, (int) ty), new Point((int) bx, (int) by)), new Scalar(255, 0, 0, 0));//print blue rectangle 
+            }
+        }
+    }
+
+    public static void main(String[] args) {
+        VideoCapture capture = new VideoCapture();
+        capture.set(CV_CAP_PROP_FRAME_WIDTH, 1280);
+        capture.set(CV_CAP_PROP_FRAME_HEIGHT, 720);
+
+        if (!capture.open(0)) {
+            System.out.println("Can not open the cam !!!");
+        }
+
+        Mat colorimg = new Mat();
+
+        CanvasFrame mainframe = new CanvasFrame("Face Detection", CanvasFrame.getDefaultGamma() / 2.2);
+        mainframe.setDefaultCloseOperation(javax.swing.JFrame.EXIT_ON_CLOSE);
+        mainframe.setCanvasSize(600, 600);
+        mainframe.setLocationRelativeTo(null);
+        mainframe.setVisible(true);
+
+        while (true) {
+            while (capture.read(colorimg) && mainframe.isVisible()) {
+                detectAndDraw(colorimg);
+                mainframe.showImage(converter.convert(colorimg));
+                try {
+                    Thread.sleep(50);
+                } catch (InterruptedException ex) {
+                    System.out.println(ex.getMessage());
+                }
+
+            }
+        }
+    }
+
+}