diff --git a/facetracker.py b/facetracker.py
index 62e8a24..1625b28 100644
--- a/facetracker.py
+++ b/facetracker.py
@@ -2,7 +2,7 @@
 import os
 #limits the threads used by numpy to 1
 #setting this and the landmarks model to use 1 means the cpu usage is pretty low
-os.environ["OMP_NUM_THREADS"] = str(1)
+
 import argparse
 import traceback
 import threading
@@ -32,10 +32,14 @@ parser.add_argument("-s", "--silent", type=int, help="Set this to 1 to prevent t
 parser.add_argument("--model", type=int, help="This can be used to select the tracking model. Higher numbers are models with better tracking quality, but slower speed, except for model 4, which is wink optimized.", default=3, choices=[0, 1, 2, 3, 4])
 parser.add_argument("--preview", type=int, help="Preview the frames sent to the tracker", default=0)
 parser.add_argument("--feature-type", type=int, help="Sets which version of feature extraction is used. 0 is my new version that works well for me and allows for some customization, 1 is EmilianaVT's version aka, normal OpenSeeFace operation", default=0, choices=[0, 1])
+parser.add_argument("--numpy-threads", type=int, help="Numer of threads Numpy can use, doesn't seem to effect much", default=1)
+parser.add_argument("--landmark-detection-threads", type=int, help="Numer of threads used for landmark detection. Default is 1 (~15ms per frame on my computer), 2 gets slightly faster frames (~10ms on my computer), more than 2 doesn't seem to help much", default=1)
+
+
 
 args = parser.parse_args()
 
-
+os.environ["OMP_NUM_THREADS"] = str(args.landmark_detection_threads)
 def messaging(messageQueue):
     while True:
         message = messageQueue.get()
diff --git a/featureExtractor.py b/featureExtractor.py
index 19b4ffb..cf47afd 100644
--- a/featureExtractor.py
+++ b/featureExtractor.py
@@ -77,10 +77,10 @@ class FeatureExtractor():
 
         f_pts = self.align_points(pts[42], pts[45], pts[[43, 44, 47, 46]])
         f = (f_pts[0][1]/2 + f_pts[1][1]/2) - (f_pts[2][1]/2 + f_pts[3][1]/2)
-        features["eye_l"] =  -self.eye_l.update(f)
+        features["eye_l"] =  -1 + self.eye_l.update(f)
         f_pts = self.align_points(pts[36], pts[39], pts[[37, 38, 41, 40]])
         f = (f_pts[0][1]/2 + f_pts[1][1]/2) - (f_pts[2][1]/2 + f_pts[3][1]/2)
-        features["eye_r"] = -self.eye_r.update(f)
+        features["eye_r"] = -1 + self.eye_r.update(f)
 
         f = ((pts[22][1]/2 + pts[26][1]/2) - (pts[42][1]/2 + pts[45][1]/2))/norm_distance_y
         features["eyebrow_updown_l"] = self.eyebrow_updown_l.update(f)