diff --git a/facelib/FaceType.py b/facelib/FaceType.py
index 816375d..044c701 100644
--- a/facelib/FaceType.py
+++ b/facelib/FaceType.py
@@ -2,6 +2,7 @@ from enum import IntEnum
 
 class FaceType(IntEnum):
     #enumerating in order "next contains prev"
+    MOUTH = -1
     HALF = 0
     MID_FULL = 1
     FULL = 2
@@ -23,7 +24,8 @@ class FaceType(IntEnum):
     def toString (face_type):
         return to_string_dict[face_type]
 
-from_string_dict = {'half_face': FaceType.HALF,
+from_string_dict = {'mouth': FaceType.MOUTH,
+                    'half_face': FaceType.HALF,
                     'midfull_face': FaceType.MID_FULL,
                     'full_face': FaceType.FULL,
                     'whole_face': FaceType.WHOLE_FACE,
@@ -32,7 +34,8 @@ from_string_dict = {'half_face': FaceType.HALF,
                     'full_face_no_align' : FaceType.FULL_NO_ALIGN,
                     'head_no_align' : FaceType.HEAD_NO_ALIGN,
                     }
-to_string_dict = { FaceType.HALF : 'half_face',
+to_string_dict = { FaceType.MOUTH : 'mouth',
+                   FaceType.HALF : 'half_face',
                    FaceType.MID_FULL : 'midfull_face',
                    FaceType.FULL : 'full_face',
                    FaceType.WHOLE_FACE : 'whole_face',
diff --git a/facelib/LandmarksProcessor.py b/facelib/LandmarksProcessor.py
index f4c1e82..b8c691c 100644
--- a/facelib/LandmarksProcessor.py
+++ b/facelib/LandmarksProcessor.py
@@ -103,6 +103,29 @@ landmarks_2D_new = np.array([
 [ 0.726104,     0.780233  ], #54
 ], dtype=np.float32)
 
+mouth_center_landmarks_2D = np.array([
+    [-4.4202591e-07,  4.4916576e-01],  #48
+    [ 1.8399176e-01,  3.7537053e-01],  #49
+    [ 3.7018123e-01,  3.3719531e-01],  #50
+    [ 5.0000089e-01,  3.6938059e-01],  #51
+    [ 6.2981832e-01,  3.3719531e-01],  #52
+    [ 8.1600773e-01,  3.7537053e-01],  #53
+    [ 1.0000000e+00,  4.4916576e-01],  #54
+    [ 8.2213330e-01,  6.2836081e-01],  #55
+    [ 6.4110327e-01,  7.0757812e-01],  #56
+    [ 5.0000089e-01,  7.2259867e-01],  #57
+    [ 3.5889623e-01,  7.0757812e-01],  #58
+    [ 1.7786618e-01,  6.2836081e-01],  #59
+    [ 7.6765373e-02,  4.5882553e-01],  #60
+    [ 3.6856663e-01,  4.4601500e-01],  #61
+    [ 5.0000089e-01,  4.5999300e-01],  #62
+    [ 6.3143289e-01,  4.4601500e-01],  #63
+    [ 9.2323411e-01,  4.5882553e-01],  #64
+    [ 6.3399029e-01,  5.4228687e-01],  #65
+    [ 5.0000089e-01,  5.5843467e-01],  #66
+    [ 3.6601129e-01,  5.4228687e-01]   #67
+], dtype=np.float32)
+
 # 68 point landmark definitions
 landmarks_68_pt = { "mouth": (48,68),
                     "right_eyebrow": (17, 22),
@@ -184,6 +207,7 @@ landmarks_68_3D = np.array( [
 [-7.198266   , 30.844876    , -20.328022  ] ], dtype=np.float32)
 
 FaceType_to_padding_remove_align = {
+    FaceType.MOUTH: (0.25, False),
     FaceType.HALF: (0.0, False),
     FaceType.MID_FULL: (0.0675, False),
     FaceType.FULL: (0.2109375, False),
@@ -254,8 +278,12 @@ def get_transform_mat (image_landmarks, output_size, face_type, scale=1.0):
     if not isinstance(image_landmarks, np.ndarray):
         image_landmarks = np.array (image_landmarks)
 
+
     # estimate landmarks transform from global space to local aligned space with bounds [0..1]
-    mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
+    if face_type == FaceType.MOUTH:
+        mat = umeyama(image_landmarks[48:68], mouth_center_landmarks_2D, True)[0:2]
+    else:
+        mat = umeyama( np.concatenate ( [ image_landmarks[17:49] , image_landmarks[54:55] ] ) , landmarks_2D_new, True)[0:2]
     
     # get corner points in global space
     g_p = transform_points (  np.float32([(0,0),(1,0),(1,1),(0,1),(0.5,0.5) ]) , mat, True)