--- a
+++ b/Code/eyebrow_detection.py
@@ -0,0 +1,92 @@
+from scipy.spatial import distance as dist
+from imutils.video import VideoStream
+from imutils import face_utils
+import numpy as np
+import imutils
+import time
+import dlib
+import cv2
+import matplotlib.pyplot as plt
+from keras.preprocessing.image import img_to_array
+from keras.models import load_model
+
+def eye_brow_distance(leye,reye):
+    global points
+    distq = dist.euclidean(leye,reye)
+    points.append(int(distq))
+    return distq
+
+def emotion_finder(faces,frame):
+    global emotion_classifier
+    EMOTIONS = ["angry" ,"disgust","scared", "happy", "sad", "surprised","neutral"]
+    x,y,w,h = face_utils.rect_to_bb(faces)
+    frame = frame[y:y+h,x:x+w]
+    roi = cv2.resize(frame,(64,64))
+    roi = roi.astype("float") / 255.0
+    roi = img_to_array(roi)
+    roi = np.expand_dims(roi,axis=0)
+    preds = emotion_classifier.predict(roi)[0]
+    emotion_probability = np.max(preds)
+    label = EMOTIONS[preds.argmax()]
+    if label in ['scared','sad']:
+        label = 'stressed'
+    else:
+        label = 'not stressed'
+    return label
+    
+def normalize_values(points,disp):
+    normalized_value = abs(disp - np.min(points))/abs(np.max(points) - np.min(points))
+    stress_value = np.exp(-(normalized_value))
+    print(stress_value)
+    if stress_value>=75:
+        return stress_value,"High Stress"
+    else:
+        return stress_value,"low_stress"
+    
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
+emotion_classifier = load_model("_mini_XCEPTION.102-0.66.hdf5", compile=False)
+cap = cv2.VideoCapture(0)
+points = []
+while(True):
+    _,frame = cap.read()
+    frame = cv2.flip(frame,1)
+    frame = imutils.resize(frame, width=500,height=500)
+    
+    
+    (lBegin, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eyebrow"]
+    (rBegin, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eyebrow"]
+
+    #preprocessing the image
+    gray = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
+    
+    detections = detector(gray,0)
+    for detection in detections:
+        emotion = emotion_finder(detection,gray)
+        cv2.putText(frame, emotion, (10,10),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+        shape = predictor(frame,detection)
+        shape = face_utils.shape_to_np(shape)
+           
+        leyebrow = shape[lBegin:lEnd]
+        reyebrow = shape[rBegin:rEnd]
+            
+        reyebrowhull = cv2.convexHull(reyebrow)
+        leyebrowhull = cv2.convexHull(leyebrow)
+
+        cv2.drawContours(frame, [reyebrowhull], -1, (0, 255, 0), 1)
+        cv2.drawContours(frame, [leyebrowhull], -1, (0, 255, 0), 1)
+
+        distq = eye_brow_distance(leyebrow[-1],reyebrow[0])
+        stress_value,stress_label = normalize_values(points,distq)
+        cv2.putText(frame,"stress level:{}".format(str(int(stress_value*100))),(20,40),cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
+
+    cv2.imshow("Frame", frame)
+
+    key = cv2.waitKey(1) & 0xFF
+    if key == ord('q'):
+        break
+cv2.destroyAllWindows()
+cap.release()
+plt.plot(range(len(points)),points,'ro')
+plt.title("Stress Levels")
+plt.show()