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+# DNN-based Face Detection And Recognition {#tutorial_dnn_face}
+
+@tableofcontents
+
+@prev_tutorial{tutorial_dnn_text_spotting}
+@next_tutorial{pytorch_cls_tutorial_dnn_conversion}
+
+| | |
+| -: | :- |
+| Original Author | Chengrui Wang, Yuantao Feng |
+| Compatibility | OpenCV >= 4.5.4 |
+
+## Introduction
+
+In this section, we introduce cv::FaceDetectorYN class for face detection and cv::FaceRecognizerSF class for face recognition.
+
+## Models
+
+There are two models (ONNX format) pre-trained and required for this module:
+- [Face Detection](https://github.com/opencv/opencv_zoo/tree/master/models/face_detection_yunet):
+    - Size: 338KB
+    - Results on WIDER Face Val set: 0.830(easy), 0.824(medium), 0.708(hard)
+- [Face Recognition](https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface)
+    - Size: 36.9MB
+    - Results:
+
+    | Database | Accuracy | Threshold (normL2) | Threshold (cosine) |
+    | -------- | -------- | ------------------ | ------------------ |
+    | LFW      | 99.60%   | 1.128              | 0.363              |
+    | CALFW    | 93.95%   | 1.149              | 0.340              |
+    | CPLFW    | 91.05%   | 1.204              | 0.275              |
+    | AgeDB-30 | 94.90%   | 1.202              | 0.277              |
+    | CFP-FP   | 94.80%   | 1.253              | 0.212              |
+
+## Code
+
+@add_toggle_cpp
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/5.x/samples/dnn/face_detect.cpp)
+
+-   **Code at glance:**
+    @include samples/dnn/face_detect.cpp
+@end_toggle
+
+@add_toggle_python
+-   **Downloadable code**: Click
+    [here](https://github.com/opencv/opencv/tree/5.x/samples/dnn/face_detect.py)
+
+-   **Code at glance:**
+    @include samples/dnn/face_detect.py
+@end_toggle
+
+Explanation
+-----------
+
+@add_toggle_cpp
+@snippet dnn/face_detect.cpp initialize_FaceDetectorYN
+@snippet dnn/face_detect.cpp inference
+@end_toggle
+
+@add_toggle_python
+@snippet dnn/face_detect.py initialize_FaceDetectorYN
+@snippet dnn/face_detect.py inference
+@end_toggle
+
+The detection output `faces` is a two-dimension array of type CV_32F, whose rows are the detected face instances, columns are the location of a face and 5 facial landmarks. The format of each row is as follows:
+
+```
+x1, y1, w, h, x_re, y_re, x_le, y_le, x_nt, y_nt, x_rcm, y_rcm, x_lcm, y_lcm
+```
+, where `x1, y1, w, h` are the top-left coordinates, width and height of the face bounding box, `{x, y}_{re, le, nt, rcm, lcm}` stands for the coordinates of right eye, left eye, nose tip, the right corner and left corner of the mouth respectively.
+
+
+### Face Recognition
+
+Following Face Detection, run codes below to extract face feature from facial image.
+
+@add_toggle_cpp
+@snippet dnn/face_detect.cpp initialize_FaceRecognizerSF
+@snippet dnn/face_detect.cpp facerecognizer
+@end_toggle
+
+@add_toggle_python
+@snippet dnn/face_detect.py initialize_FaceRecognizerSF
+@snippet dnn/face_detect.py facerecognizer
+@end_toggle
+
+After obtaining face features *feature1* and *feature2* of two facial images, run codes below to calculate the identity discrepancy between the two faces.
+
+@add_toggle_cpp
+@snippet dnn/face_detect.cpp match
+@end_toggle
+
+@add_toggle_python
+@snippet dnn/face_detect.py match
+@end_toggle
+
+For example, two faces have same identity if the cosine distance is greater than or equal to 0.363, or the normL2 distance is less than or equal to 1.128.
+
+## Reference:
+
+- https://github.com/ShiqiYu/libfacedetection
+- https://github.com/ShiqiYu/libfacedetection.train
+- https://github.com/zhongyy/SFace
+
+## Acknowledgement
+
+Thanks [Professor Shiqi Yu](https://github.com/ShiqiYu/) and [Yuantao Feng](https://github.com/fengyuentau) for training and providing the face detection model.
+
+Thanks [Professor Deng](http://www.whdeng.cn/), [PhD Candidate Zhong](https://github.com/zhongyy/) and [Master Candidate Wang](https://github.com/crywang/) for training and providing the face recognition model.