diff --git a/facefusion/face_analyser.py b/facefusion/face_analyser.py
index 51b26206..a4e257a1 100644
--- a/facefusion/face_analyser.py
+++ b/facefusion/face_analyser.py
@@ -8,7 +8,7 @@ import onnxruntime
 import facefusion.globals
 from facefusion import process_manager
 from facefusion.common_helper import get_first
-from facefusion.face_helper import warp_face_by_face_landmark_5, warp_face_by_translation, create_static_anchors, distance_to_face_landmark_5, distance_to_bounding_box, convert_face_landmark_68_to_5, apply_nms, categorize_age, categorize_gender, WARP_TEMPLATES
+from facefusion.face_helper import estimate_matrix_by_face_landmark_5, warp_face_by_face_landmark_5, warp_face_by_translation, create_static_anchors, distance_to_face_landmark_5, distance_to_bounding_box, convert_face_landmark_68_to_5, apply_nms, categorize_age, categorize_gender
 from facefusion.face_store import get_static_faces, set_static_faces
 from facefusion.execution import apply_execution_provider_options
 from facefusion.download import conditional_download
@@ -399,8 +399,7 @@ def detect_face_landmark_68(temp_vision_frame : VisionFrame, bounding_box : Boun
 
 def expand_face_landmark_68_from_5(face_landmark_5 : FaceLandmark5) -> FaceLandmark68:
 	face_landmarker = get_face_analyser().get('face_landmarkers').get('68_5')
-	normed_warp_template = WARP_TEMPLATES.get('ffhq_512') * 512
-	affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
+	affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, 'ffhq_512', (512, 512))
 	face_landmark_5 = cv2.transform(face_landmark_5.reshape(1, -1, 2), affine_matrix).reshape(-1, 2)
 	face_landmark_5 = face_landmark_5 / 512
 	face_landmark_68_5 = face_landmarker.run(None,
diff --git a/facefusion/face_helper.py b/facefusion/face_helper.py
index 7c2ad4ad..d0697db5 100644
--- a/facefusion/face_helper.py
+++ b/facefusion/face_helper.py
@@ -43,9 +43,14 @@ WARP_TEMPLATES : WarpTemplateSet =\
 }
 
 
-def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
+def estimate_matrix_by_face_landmark_5(face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Matrix:
 	normed_warp_template = WARP_TEMPLATES.get(warp_template) * crop_size
 	affine_matrix = cv2.estimateAffinePartial2D(face_landmark_5, normed_warp_template, method = cv2.RANSAC, ransacReprojThreshold = 100)[0]
+	return affine_matrix
+
+
+def warp_face_by_face_landmark_5(temp_vision_frame : VisionFrame, face_landmark_5 : FaceLandmark5, warp_template : WarpTemplate, crop_size : Size) -> Tuple[VisionFrame, Matrix]:
+	affine_matrix = estimate_matrix_by_face_landmark_5(face_landmark_5, warp_template, crop_size)
 	crop_vision_frame = cv2.warpAffine(temp_vision_frame, affine_matrix, crop_size, borderMode = cv2.BORDER_REPLICATE, flags = cv2.INTER_AREA)
 	return crop_vision_frame, affine_matrix