diff --git a/face detect/demo - detect.py b/face detect/demo - detect.py
new file mode 100644
index 0000000..a18f0d0
--- /dev/null
+++ b/face detect/demo - detect.py	
@@ -0,0 +1,120 @@
+#!/usr/bin/python
+'''
+    Author: Guido Diepen <gdiepen@deloitte.nl>
+'''
+
+#Import the OpenCV library
+import cv2
+
+#Initialize a face cascade using the frontal face haar cascade provided with
+#the OpenCV library
+faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
+
+#The deisred output width and height
+OUTPUT_SIZE_WIDTH = 775
+OUTPUT_SIZE_HEIGHT = 600
+
+
+
+def detectLargestFace():
+    #Open the first webcame device
+    capture = cv2.VideoCapture(0)
+
+    #Create two opencv named windows
+    cv2.namedWindow("base-image", cv2.WINDOW_AUTOSIZE)
+    cv2.namedWindow("result-image", cv2.WINDOW_AUTOSIZE)
+
+    #Position the windows next to eachother
+    cv2.moveWindow("base-image",0,100)
+    cv2.moveWindow("result-image",400,100)
+
+    #Start the window thread for the two windows we are using
+    cv2.startWindowThread()
+
+    rectangleColor = (0,165,255)
+
+    try:
+        while True:
+            #Retrieve the latest image from the webcam
+            rc,fullSizeBaseImage = capture.read()
+
+            #Resize the image to 320x240
+            baseImage = cv2.resize( fullSizeBaseImage, ( 320, 240))
+
+
+            #Check if a key was pressed and if it was Q, then destroy all
+            #opencv windows and exit the application
+            pressedKey = cv2.waitKey(2)
+            if pressedKey == ord('Q'):
+                cv2.destroyAllWindows()
+                exit(0)
+
+
+
+            #Result image is the image we will show the user, which is a
+            #combination of the original image from the webcam and the
+            #overlayed rectangle for the largest face
+            resultImage = baseImage.copy()
+
+
+            #For the face detection, we need to make use of a gray colored
+            #image so we will convert the baseImage to a gray-based image
+            gray = cv2.cvtColor(baseImage, cv2.COLOR_BGR2GRAY)
+            #Now use the haar cascade detector to find all faces in the
+            #image
+            faces = faceCascade.detectMultiScale(gray, 1.3, 5)
+
+
+            #For now, we are only interested in the 'largest' face, and we
+            #determine this based on the largest area of the found
+            #rectangle. First initialize the required variables to 0
+            maxArea = 0
+            x = 0
+            y = 0
+            w = 0
+            h = 0
+
+
+            #Loop over all faces and check if the area for this face is
+            #the largest so far
+            for (_x,_y,_w,_h) in faces:
+                if  _w*_h > maxArea:
+                    x = _x
+                    y = _y
+                    w = _w
+                    h = _h
+                    maxArea = w*h
+
+            #If one or more faces are found, draw a rectangle around the
+            #largest face present in the picture
+            if maxArea > 0 :
+                cv2.rectangle(resultImage,  (x-10, y-20),
+                                            (x + w+10 , y + h+20),
+                                            rectangleColor,2)
+
+
+
+            #Since we want to show something larger on the screen than the
+            #original 320x240, we resize the image again
+            #
+            #Note that it would also be possible to keep the large version
+            #of the baseimage and make the result image a copy of this large
+            #base image and use the scaling factor to draw the rectangle
+            #at the right coordinates.
+            largeResult = cv2.resize(resultImage,
+                                     (OUTPUT_SIZE_WIDTH,OUTPUT_SIZE_HEIGHT))
+
+            #Finally, we want to show the images on the screen
+            cv2.imshow("base-image", baseImage)
+            cv2.imshow("result-image", largeResult)
+
+    #To ensure we can also deal with the user pressing Ctrl-C in the console
+    #we have to check for the KeyboardInterrupt exception and destroy
+    #all opencv windows and exit the application
+    except KeyboardInterrupt as e:
+        cv2.destroyAllWindows()
+        exit(0)
+
+
+if __name__ == '__main__':
+    detectLargestFace()