opencv/doc/tutorials/dnn/dnn_face/dnn_face.markdown

DNN-based Face Detection And Recognition

@tableofcontents

@prev_tutorial{tutorial_dnn_text_spotting} @next_tutorial{pytorch_cls_tutorial_dnn_conversion}

Original Author	Chengrui Wang, Yuantao Feng
Compatibility	OpenCV >= 4.5.1

Introduction

In this section, we introduce the DNN-based module for face detection and face recognition. Models can be obtained in Models. The usage of FaceDetectorYN and FaceRecognizerSF are presented in Usage.

Models

There are two models (ONNX format) pre-trained and required for this module:

• Face Detection:
  • Size: 337KB
  • Results on WIDER Face Val set: 0.830(easy), 0.824(medium), 0.708(hard)
• Face Recognition
  • 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

Usage

DNNFaceDetector

@add_toggle_cpp

• Downloadable code: Click here

• Code at glance: @include samples/dnn/face_detect.cpp @end_toggle

@add_toggle_python

• Downloadable code: Click here

• 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 and Yuantao Feng for training and providing the face detection model.

Thanks Professor Deng, PhD Candidate Zhong and Master Candidate Wang for training and providing the face recognition model.