diff --git a/facelib/LandmarksProcessor.py b/facelib/LandmarksProcessor.py
index 3b8b547..3495403 100644
--- a/facelib/LandmarksProcessor.py
+++ b/facelib/LandmarksProcessor.py
@@ -9,25 +9,96 @@ from mathlib.umeyama import umeyama
 from facelib import FaceType
 import math
 
-mean_face_x = np.array([
-0.000213256, 0.0752622, 0.18113, 0.29077, 0.393397, 0.586856, 0.689483, 0.799124,
-0.904991, 0.98004, 0.490127, 0.490127, 0.490127, 0.490127, 0.36688, 0.426036,
-0.490127, 0.554217, 0.613373, 0.121737, 0.187122, 0.265825, 0.334606, 0.260918,
-0.182743, 0.645647, 0.714428, 0.793132, 0.858516, 0.79751, 0.719335, 0.254149,
-0.340985, 0.428858, 0.490127, 0.551395, 0.639268, 0.726104, 0.642159, 0.556721,
-0.490127, 0.423532, 0.338094, 0.290379, 0.428096, 0.490127, 0.552157, 0.689874,
-0.553364, 0.490127, 0.42689 ])
-
-mean_face_y = np.array([
-0.106454, 0.038915, 0.0187482, 0.0344891, 0.0773906, 0.0773906, 0.0344891,
-0.0187482, 0.038915, 0.106454, 0.203352, 0.307009, 0.409805, 0.515625, 0.587326,
-0.609345, 0.628106, 0.609345, 0.587326, 0.216423, 0.178758, 0.179852, 0.231733,
-0.245099, 0.244077, 0.231733, 0.179852, 0.178758, 0.216423, 0.244077, 0.245099,
-0.780233, 0.745405, 0.727388, 0.742578, 0.727388, 0.745405, 0.780233, 0.864805,
-0.902192, 0.909281, 0.902192, 0.864805, 0.784792, 0.778746, 0.785343, 0.778746,
-0.784792, 0.824182, 0.831803, 0.824182 ])
-
-landmarks_2D = np.stack( [ mean_face_x, mean_face_y ], axis=1 )
+landmarks_2D = np.array([
+[ 0.000213256,  0.106454  ], #17
+[ 0.0752622,    0.038915  ], #18
+[ 0.18113,      0.0187482 ], #19
+[ 0.29077,      0.0344891 ], #20
+[ 0.393397,     0.0773906 ], #21
+[ 0.586856,     0.0773906 ], #22
+[ 0.689483,     0.0344891 ], #23
+[ 0.799124,     0.0187482 ], #24
+[ 0.904991,     0.038915  ], #25
+[ 0.98004,      0.106454  ], #26
+[ 0.490127,     0.203352  ], #27
+[ 0.490127,     0.307009  ], #28
+[ 0.490127,     0.409805  ], #29
+[ 0.490127,     0.515625  ], #30
+[ 0.36688,      0.587326  ], #31
+[ 0.426036,     0.609345  ], #32
+[ 0.490127,     0.628106  ], #33
+[ 0.554217,     0.609345  ], #34
+[ 0.613373,     0.587326  ], #35
+[ 0.121737,     0.216423  ], #36
+[ 0.187122,     0.178758  ], #37
+[ 0.265825,     0.179852  ], #38
+[ 0.334606,     0.231733  ], #39
+[ 0.260918,     0.245099  ], #40
+[ 0.182743,     0.244077  ], #41
+[ 0.645647,     0.231733  ], #42
+[ 0.714428,     0.179852  ], #43
+[ 0.793132,     0.178758  ], #44
+[ 0.858516,     0.216423  ], #45
+[ 0.79751,      0.244077  ], #46
+[ 0.719335,     0.245099  ], #47
+[ 0.254149,     0.780233  ], #48
+[ 0.340985,     0.745405  ], #49
+[ 0.428858,     0.727388  ], #50
+[ 0.490127,     0.742578  ], #51
+[ 0.551395,     0.727388  ], #52
+[ 0.639268,     0.745405  ], #53
+[ 0.726104,     0.780233  ], #54
+[ 0.642159,     0.864805  ], #55
+[ 0.556721,     0.902192  ], #56
+[ 0.490127,     0.909281  ], #57
+[ 0.423532,     0.902192  ], #58
+[ 0.338094,     0.864805  ], #59
+[ 0.290379,     0.784792  ], #60
+[ 0.428096,     0.778746  ], #61
+[ 0.490127,     0.785343  ], #62
+[ 0.552157,     0.778746  ], #63
+[ 0.689874,     0.784792  ], #64
+[ 0.553364,     0.824182  ], #65
+[ 0.490127,     0.831803  ], #66
+[ 0.42689 ,     0.824182  ]  #67
+], dtype=np.float32)                        
+             
+            
+landmarks_2D_new = np.array([
+[ 0.000213256,  0.106454  ], #17
+[ 0.0752622,    0.038915  ], #18
+[ 0.18113,      0.0187482 ], #19
+[ 0.29077,      0.0344891 ], #20
+[ 0.393397,     0.0773906 ], #21
+[ 0.586856,     0.0773906 ], #22
+[ 0.689483,     0.0344891 ], #23
+[ 0.799124,     0.0187482 ], #24
+[ 0.904991,     0.038915  ], #25
+[ 0.98004,      0.106454  ], #26
+[ 0.490127,     0.203352  ], #27
+[ 0.490127,     0.307009  ], #28
+[ 0.490127,     0.409805  ], #29
+[ 0.490127,     0.515625  ], #30
+[ 0.36688,      0.587326  ], #31
+[ 0.426036,     0.609345  ], #32
+[ 0.490127,     0.628106  ], #33
+[ 0.554217,     0.609345  ], #34
+[ 0.613373,     0.587326  ], #35
+[ 0.121737,     0.216423  ], #36
+[ 0.187122,     0.178758  ], #37
+[ 0.265825,     0.179852  ], #38
+[ 0.334606,     0.231733  ], #39
+[ 0.260918,     0.245099  ], #40
+[ 0.182743,     0.244077  ], #41
+[ 0.645647,     0.231733  ], #42
+[ 0.714428,     0.179852  ], #43
+[ 0.793132,     0.178758  ], #44
+[ 0.858516,     0.216423  ], #45
+[ 0.79751,      0.244077  ], #46
+[ 0.719335,     0.245099  ], #47
+[ 0.254149,     0.780233  ], #48
+[ 0.726104,     0.780233  ], #54
+], dtype=np.float32)                  
 
 # 68 point landmark definitions
 landmarks_68_pt = { "mouth": (48,68),
@@ -155,7 +226,9 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
     else:
         raise ValueError ('wrong face_type: ', face_type)
 
-    mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
+    #mat = umeyama(image_landmarks[17:], landmarks_2D, True)[0:2]
+    mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
+    
     mat = mat * (output_size - 2 * padding)
     mat[:,2] += padding
     mat *= (1 / scale)