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DNN-based Face Detection And Recognition

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Original Author Chengrui Wang, Yuantao Feng
Compatibility OpenCV >= 4.5.4


In this section, we introduce cv::FaceDetectorYN class for face detection and cv::FaceRecognizerSF class for face recognition.


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

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



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.

After obtaining face features feature1 and feature2 of two facial images, run codes below to calculate the identity discrepancy between the two faces.

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.



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.