diff --git a/server/cluster.py b/server/cluster.py
index 9db31e9..b56049a 100644
--- a/server/cluster.py
+++ b/server/cluster.py
@@ -7,8 +7,7 @@ from sqlite3 import Error
 from PIL import Image
 import numpy as np
 from deepface import DeepFace
-from retinaface import RetinaFace
-
+from deepface.detectors import FaceDetector
 
 sqlite3.register_adapter(np.array, lambda arr: arr.tobytes())    
 sqlite3.register_converter("array", np.frombuffer)
@@ -21,8 +20,11 @@ class NpEncoder(json.JSONEncoder):
       return float(obj)
     if isinstance(obj, np.ndarray):
       return obj.tolist()
-        
-model = DeepFace.build_model('ArcFace')
+
+model_name = 'VGG-Face'    # 'ArcFace'
+detector_backend = 'mtcnn' # 'retinaface'
+model = DeepFace.build_model(model_name)
+face_detector = FaceDetector.build_model(detector_backend)
 input_shape = DeepFace.functions.find_input_shape(model)
 
 def create_connection(db_file):
@@ -134,7 +136,7 @@ with conn:
     SELECT faces.id,facedescriptors.descriptors,faces.faceConfidence,faces.photoId
     FROM faces
     JOIN facedescriptors ON (faces.descriptorId=facedescriptors.id)
-    WHERE faces.identityId IS null
+    WHERE faces.identityId IS null AND faces.faceConfidence>0.945
     ''')
   for row in res.fetchall():
     id, descriptors, confidence, photoId = row
@@ -162,28 +164,35 @@ with conn:
       if face2['scanned']:
         continue
       face = {
-        'between': (face1['id'], face2['id'])
+        'between': (face1['id'], face2['id']),
+        'confidence': (face1['confidence'], face2['confidence'])
       }
 
       face['distanceCosine'] = findCosineDistance(
         face1['descriptors'],
         face2['descriptors']
       )
-      face['distanceEuclidian'] = findEuclideanDistance(
+      face['distanceEuclidean'] = findEuclideanDistance(
         face1['descriptors'],
         face2['descriptors']
       )
-      face['distanceEuclidianL2'] = findEuclideanDistance(
+      face['distanceEuclideanL2'] = findEuclideanDistance(
         l2_normalize(face1['descriptors']),
         l2_normalize(face2['descriptors'])
       )
 
       face['scoring'] = 0
-      if face['distanceCosine'] < 0.65:
+
+      if model_name == 'VGG-Face':
+        thresholds = {'cosine': 0.40, 'euclidean': 0.60, 'euclidean_l2': 0.86}
+      elif model_name == 'ArcFace':
+        thresholds = {'cosine': 0.68, 'euclidean': 4.15, 'euclidean_l2': 1.13}
+
+      if face['distanceCosine'] < thresholds['cosine']:
         face['scoring'] += 1
-      if face['distanceEuclidian'] < 4.00:
+      if face['distanceEuclidean'] < thresholds['euclidean']:
         face['scoring'] += 1
-      if face['distanceEuclidianL2'] < 1.055:
+      if face['distanceEuclideanL2'] < thresholds['euclidean_l2']:
         face['scoring'] += 1
 
       if face['scoring'] == 3: # Same face!
@@ -214,11 +223,13 @@ with conn:
     for member in identity['members']:
       face1 = member['between'][0]
       face2 = member['between'][1]
+      path1 = f'faces/{"{:02d}".format(face1 % 10)}'
+      path2 = f'faces/{"{:02d}".format(face2 % 10)}'
       print('<div>')
-      print(f'<img src="faces/{face1%10}/{face1}.jpg"/>')
-      print(f'<img src="faces/{face2%10}/{face2}.jpg"/>')
+      print(f'<img src="{path1}/{face1}.jpg"/>{member["confidence"][0]}')
+      print(f'<img src="{path2}/{face2}.jpg"/>{member["confidence"][1]}')
       print('</div>')
-      print(f'<div>Distance: {member["distanceCosine"]}, {member["distanceEuclidian"]}, {member["distanceEuclidianL2"]}</div>')
+      print(f'<div>Distance: {member["distanceCosine"]}, {member["distanceEuclidean"]}, {member["distanceEuclideanL2"]}</div>')
     print('</div>')
     print('</div>')
 
diff --git a/server/detect.py b/server/detect.py
index af64a23..6a59d51 100644
--- a/server/detect.py
+++ b/server/detect.py
@@ -52,8 +52,6 @@ class NpEncoder(json.JSONEncoder):
       return float(obj)
     if isinstance(obj, np.ndarray):
       return obj.tolist()
-        
-models = ["VGG-Face", "Facenet", "Facenet512", "OpenFace", "DeepFace", "DeepID", "ArcFace", "Dlib", "SFace"]
 
 model_name = 'VGG-Face'    # 'ArcFace'
 detector_backend = 'mtcnn' # 'retinaface'