diff --git a/facelib/MTCExtractor.py b/facelib/MTCExtractor.py
index a43829b..e5f630a 100644
--- a/facelib/MTCExtractor.py
+++ b/facelib/MTCExtractor.py
@@ -90,7 +90,7 @@ class MTCExtractor(object):
         input_image = input_image[:,:,::-1].copy()
         (h, w, ch) = input_image.shape
 
-        input_scale = self.scale_to / (w if w > h else h)
+        input_scale = self.scale_to / max(w,h)
         input_image = cv2.resize (input_image, ( int(w*input_scale), int(h*input_scale) ), interpolation=cv2.INTER_LINEAR)
 
         detected_faces, pnts = detect_face ( input_image, self.min_face_size, self.pnet_fun, self.rnet_fun, self.onet_fun, [ self.thresh1, self.thresh2, self.thresh3 ], self.scale_factor )
diff --git a/facelib/S3FDExtractor.py b/facelib/S3FDExtractor.py
index 3763cf2..b7aa80a 100644
--- a/facelib/S3FDExtractor.py
+++ b/facelib/S3FDExtractor.py
@@ -5,8 +5,6 @@ from nnlib import nnlib
 
 class S3FDExtractor(object):   
     def __init__(self):
-        self.scale_factor = 3
-        
         exec( nnlib.import_all(), locals(), globals() )
         
         model_path = Path(__file__).parent / "S3FD.h5"
@@ -25,24 +23,30 @@ class S3FDExtractor(object):
         input_image = input_image[:,:,::-1].copy()
         (h, w, ch) = input_image.shape
         
-        d = w if w > h else h
-        input_scale = d / ( (d // self.scale_factor) - (d % self.scale_factor) )
+        d = max(w, h)
+        scale_to = 640 if d >= 1280 else d / 2
+        scale_to = max(64, scale_to)
+            
+        input_scale = d / scale_to
         input_image = cv2.resize (input_image, ( int(w/input_scale), int(h/input_scale) ), interpolation=cv2.INTER_LINEAR)
 
         olist = self.model.predict( np.expand_dims(input_image,0) )
         
         detected_faces = self.refine (olist)
         
+        #filtering faces < 40pix by any side
         #enlarging bottom line a bit for 2DFAN-4, because default is not enough covering a chin
-        for face in detected_faces:
-            l,t,r,b = face
-            tb = (b-t) * 0.1
-            face[3] += tb
-        
+        new_detected_faces = []
+        for l,t,r,b in detected_faces:
+            bt = b-t
+            if min(r-l,bt) < 40:
+                continue
+            new_detected_faces.append ((l,t,r,b+bt*0.1))
+
         return [ (int(face[0]*input_scale), 
                   int(face[1]*input_scale),
                   int(face[2]*input_scale), 
-                  int(face[3]*input_scale)) for face in detected_faces ]
+                  int(face[3]*input_scale)) for face in new_detected_faces ]
         
     def refine(self, olist):
         bboxlist = []
diff --git a/mainscripts/Extractor.py b/mainscripts/Extractor.py
index 28fa105..03b7cf4 100644
--- a/mainscripts/Extractor.py
+++ b/mainscripts/Extractor.py
@@ -79,13 +79,24 @@ class ExtractSubprocessor(Subprocessor):
                 image = self.cached_image[1]
             else:
                 image = cv2_imread( filename_path_str )
+                h, w, ch = image.shape
+                wm = w % 2
+                hm = h % 2
+                if wm + hm != 0: #fix odd image
+                    image = image[0:h-hm,0:w-wm,:]
                 self.cached_image = ( filename_path_str, image )
             
             if image is None:
                 self.log_err ( 'Failed to extract %s, reason: cv2_imread() fail.' % ( str(filename_path) ) )
             else:
                 if self.type == 'rects':
-                    rects = self.e.extract_from_bgr (image)
+                    h, w, ch = image.shape
+                    if min(w,h) < 128:
+                        self.log_err ( 'Image is too small %s : [%d, %d]' % ( str(filename_path), w, h ) )
+                        rects = []
+                    else:                    
+                        rects = self.e.extract_from_bgr (image)
+                        
                     return [str(filename_path), rects]
 
                 elif self.type == 'landmarks':