diff --git a/demos/classifier_webcam.py b/demos/classifier_webcam.py
index 122bfb6..c622243 100755
--- a/demos/classifier_webcam.py
+++ b/demos/classifier_webcam.py
@@ -99,7 +99,7 @@ def getRep(bgrImg):
             time.time() - start))
 
     # print (reps)
-    return reps
+    return (reps,bb)
 
 
 def infer(img, args):
@@ -109,7 +109,9 @@ def infer(img, args):
         else:
                 (le, clf) = pickle.load(f, encoding='latin1')  # le - label and clf - classifer
 
-    reps = getRep(img)
+    repsAndBBs = getRep(img)
+    reps = repsAndBBs[0]
+    bbs = repsAndBBs[1]
     persons = []
     confidences = []
     for rep in reps:
@@ -135,7 +137,7 @@ def infer(img, args):
             dist = np.linalg.norm(rep - clf.means_[maxI])
             print("  + Distance from the mean: {}".format(dist))
             pass
-    return (persons, confidences)
+    return (persons, confidences ,bbs)
 
 
 if __name__ == '__main__':
@@ -188,7 +190,7 @@ if __name__ == '__main__':
     confidenceList = []
     while True:
         ret, frame = video_capture.read()
-        persons, confidences = infer(frame, args)
+        persons, confidences, bbs = infer(frame, args)
         print ("P: " + str(persons) + " C: " + str(confidences))
         try:
             # append with two floating point precision
@@ -202,9 +204,13 @@ if __name__ == '__main__':
             if c <= args.threshold:  # 0.5 is kept as threshold for known face.
                 persons[i] = "_unknown"
 
-                # Print the person name and conf value on the frame
-        cv2.putText(frame, "P: {} C: {}".format(persons, confidences),
-                    (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+        # Print the person name and conf value on the frame next to the person
+        # Also print the bounding box
+        for idx,person in enumerate(persons):
+            cv2.rectangle(frame, (bbs[idx].left(), bbs[idx].top()), (bbs[idx].right(), bbs[idx].bottom()), (0, 255, 0), 2)
+            cv2.putText(frame, "{} @{:.2f}".format(person, confidences[idx]),
+                        (bbs[idx].left(), bbs[idx].bottom()+20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+
         cv2.imshow('', frame)
         # quit the program on the press of key 'q'
         if cv2.waitKey(1) & 0xFF == ord('q'):