diff --git a/facelib/LandmarksExtractor.py b/facelib/LandmarksExtractor.py
index 810c81a..b61298c 100644
--- a/facelib/LandmarksExtractor.py
+++ b/facelib/LandmarksExtractor.py
@@ -3,7 +3,8 @@ import numpy as np
 import os
 import cv2
 from pathlib import Path
-
+from facelib import FaceType
+from facelib import LandmarksProcessor
 
 class LandmarksExtractor(object):
     def __init__ (self, keras):
@@ -23,7 +24,7 @@ class LandmarksExtractor(object):
         del self.keras_model
         return False #pass exception between __enter__ and __exit__ to outter level
         
-    def extract_from_bgr (self, input_image, rects):
+    def extract_from_bgr (self, input_image, rects, second_pass_extractor=None):
         input_image = input_image[:,:,::-1].copy()
         (h, w, ch) = input_image.shape
         
@@ -44,10 +45,29 @@ class LandmarksExtractor(object):
                 landmarks.append ( ( (left, top, right, bottom),pts_img ) )
             except Exception as e:
                 print ("extract_from_bgr: ", traceback.format_exc() )
-                landmarks.append ( ( (left, top, right, bottom), [ (0,0) for _ in range(68) ] ) )
+                landmarks.append ( ( (left, top, right, bottom), None ) )
+
+        if second_pass_extractor is not None:
+            for i in range(len(landmarks)):                
+                rect, lmrks = landmarks[i]
+                if lmrks is None:
+                    continue
+                    
+                image_to_face_mat = LandmarksProcessor.get_transform_mat (lmrks, 256, FaceType.FULL)
+                face_image = cv2.warpAffine(input_image, image_to_face_mat, (256, 256), cv2.INTER_CUBIC)
+                
+                rects2 = second_pass_extractor.extract_from_bgr(face_image)
+                if len(rects2) != 1: #dont do second pass if more than 1 face detected in cropped image
+                    continue
+                    
+                rect2 = rects2[0]
+                
+                lmrks2 = self.extract_from_bgr (face_image, [rect2] )[0][1]
+                source_lmrks2 = LandmarksProcessor.transform_points (lmrks2, image_to_face_mat, True)                            
+                landmarks[i] = (rect, source_lmrks2)               
                 
         return landmarks
-        
+
     def transform(self, point, center, scale, resolution):
         pt = np.array ( [point[0], point[1], 1.0] )            
         h = 200.0 * scale
diff --git a/mainscripts/Extractor.py b/mainscripts/Extractor.py
index e9b78bd..b21216f 100644
--- a/mainscripts/Extractor.py
+++ b/mainscripts/Extractor.py
@@ -52,7 +52,7 @@ class ExtractSubprocessor(Subprocessor):
                         nnlib.import_all (device_config)
                         self.e = facelib.S3FDExtractor()
                     else:
-                        raise ValueError ("Wrond detector type.")
+                        raise ValueError ("Wrong detector type.")
                         
                     if self.e is not None:
                         self.e.__enter__()
@@ -61,6 +61,11 @@ class ExtractSubprocessor(Subprocessor):
                 nnlib.import_all (device_config)
                 self.e = facelib.LandmarksExtractor(nnlib.keras)
                 self.e.__enter__()
+                if device_config.gpu_vram_gb[0] >= 2:
+                    self.second_pass_e = facelib.S3FDExtractor()
+                    self.second_pass_e.__enter__()
+                else:
+                    self.second_pass_e = None
                 
             elif self.type == 'final':
                 pass
@@ -76,7 +81,7 @@ class ExtractSubprocessor(Subprocessor):
 
             filename_path_str = str(filename_path)
             if self.cached_image[0] == filename_path_str:
-                image = self.cached_image[1]
+                image = self.cached_image[1] #cached image for manual extractor
             else:
                 image = cv2_imread( filename_path_str )
                 
@@ -102,8 +107,14 @@ class ExtractSubprocessor(Subprocessor):
                     image = image[0:h-hm,0:w-wm,:]
                 self.cached_image = ( filename_path_str, image )
        
+            src_dflimg = None
+            h, w, ch = image.shape                
+            if h == w:
+                #extracting from already extracted jpg image?
+                if filename_path.suffix == '.jpg':
+                    src_dflimg = DFLJPG.load ( str(filename_path) )
+       
             if self.type == 'rects':
-                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 = []
@@ -116,18 +127,13 @@ class ExtractSubprocessor(Subprocessor):
                 rects = data[1]
                 if rects is None:
                     landmarks = None
-                else:
-                    landmarks = self.e.extract_from_bgr (image, rects)                    
+                else:                    
+                    landmarks = self.e.extract_from_bgr (image, rects, self.second_pass_e if src_dflimg is None else None)                    
                     
                 return [str(filename_path), landmarks]
 
             elif self.type == 'final':
-                src_dflimg = None
-                (h,w,c) = image.shape                
-                if h == w:
-                    #extracting from already extracted jpg image?
-                    if filename_path.suffix == '.jpg':
-                        src_dflimg = DFLJPG.load ( str(filename_path) )
+                
             
                 result = []
                 faces = data[1]
@@ -139,7 +145,10 @@ class ExtractSubprocessor(Subprocessor):
                 face_idx = 0
                 for face in faces:   
                     rect = np.array(face[0])
-                    image_landmarks = np.array(face[1])
+                    image_landmarks = face[1]
+                    if image_landmarks is None:
+                        continue
+                    image_landmarks = np.array(image_landmarks)
 
                     if self.face_type == FaceType.MARK_ONLY:                        
                         face_image = image
diff --git a/nnlib/device.py b/nnlib/device.py
index 67f1901..a0d9e4f 100644
--- a/nnlib/device.py
+++ b/nnlib/device.py
@@ -64,6 +64,7 @@ class device:
                             
                 self.cpu_only = (len(self.gpu_idxs) == 0)
  
+            
             if not self.cpu_only:
                 self.gpu_names = []
                 self.gpu_compute_caps = []
@@ -73,7 +74,11 @@ class device:
                     self.gpu_compute_caps += [ device.getDeviceComputeCapability(gpu_idx) ]
                     self.gpu_vram_gb += [ device.getDeviceVRAMTotalGb(gpu_idx) ]
                 self.cpu_only = (len(self.gpu_idxs) == 0)
-                
+            else:
+                self.gpu_names = ['CPU']
+                self.gpu_compute_caps = [99]
+                self.gpu_vram_gb = [0]
+            
             if self.cpu_only:
                 self.backend = "tensorflow-cpu"