diff --git a/doc/tutorials/dnn/dnn_face/dnn_face.markdown b/doc/tutorials/dnn/dnn_face/dnn_face.markdown
index e5092b8b92e8..202be3e0e302 100644
--- a/doc/tutorials/dnn/dnn_face/dnn_face.markdown
+++ b/doc/tutorials/dnn/dnn_face/dnn_face.markdown
@@ -12,7 +12,7 @@
 
 ## Introduction
 
-In this section, we introduce the DNN-based module for face detection and face recognition. Models can be obtained in [Models](#Models). The usage of `FaceDetectorYN` and `FaceRecognizer` are presented in [Usage](#Usage).
+In this section, we introduce the DNN-based module for face detection and face recognition. Models can be obtained in [Models](#Models). The usage of `FaceDetectorYN` and `FaceRecognizerSF` are presented in [Usage](#Usage).
 
 ## Models
 
@@ -58,8 +58,8 @@ x1, y1, w, h, x_re, y_re, x_le, y_le, x_nt, y_nt, x_rcm, y_rcm, x_lcm, y_lcm
 Following Face Detection, run codes below to extract face feature from facial image.
 
 ```cpp
-// Initialize FaceRecognizer with model path (cv::String)
-Ptr<FaceRecognizer> faceRecognizer = FaceRecognizer::create(model_path, "");
+// Initialize FaceRecognizerSF with model path (cv::String)
+Ptr<FaceRecognizerSF> faceRecognizer = FaceRecognizerSF::create(model_path, "");
 
 // Aligning and cropping facial image through the first face of faces detected by dnn_face::DNNFaceDetector
 Mat aligned_face;
diff --git a/samples/dnn/face_match.py b/samples/dnn/face_match.py
index b36c9f6367af..916c76abf1e8 100644
--- a/samples/dnn/face_match.py
+++ b/samples/dnn/face_match.py
@@ -38,20 +38,20 @@
 face2_align = recognizer.alignCrop(img2, face2[1][0])
 
 # Extract features
-face1_feature = recognizer.faceFeature(face1_align)
-face2_feature = recognizer.faceFeature(face2_align)
+face1_feature = recognizer.feature(face1_align)
+face2_feature = recognizer.feature(face2_align)
 
 # Calculate distance (0: cosine, 1: L2)
 cosine_similarity_threshold = 0.363
-cosine_score = recognizer.faceMatch(face1_feature, face2_feature, 0)
+cosine_score = recognizer.match(face1_feature, face2_feature, 0)
 msg = 'different identities'
 if cosine_score >= cosine_similarity_threshold:
     msg = 'the same identity'
 print('They have {}. Cosine Similarity: {}, threshold: {} (higher value means higher similarity, max 1.0).'.format(msg, cosine_score, cosine_similarity_threshold))
 
 l2_similarity_threshold = 1.128
-l2_score = recognizer.faceMatch(face1_feature, face2_feature, 1)
+l2_score = recognizer.match(face1_feature, face2_feature, 1)
 msg = 'different identities'
 if l2_score <= l2_similarity_threshold:
     msg = 'the same identity'
-print('They have {}. NormL2 Distance: {}, threshold: {} (lower value means higher similarity, min 0.0).'.format(msg, l2_score, l2_similarity_threshold))
\ No newline at end of file
+print('They have {}. NormL2 Distance: {}, threshold: {} (lower value means higher similarity, min 0.0).'.format(msg, l2_score, l2_similarity_threshold))