libfacedetection

This is an open source library for CNN-based face detection in images. The CNN model has been converted to static variables in C source files. The source code does not depend on any other libraries. What you need is just a C++ compiler. You can compile the source code under Windows, Linux, ARM and any platform with a C++ compiler.

SIMD instructions are used to speed up the detection. You can enable AVX2 if you use Intel CPU or NEON for ARM.

The model files are provided in src/facedetectcnn-data.cpp (C++ arrays) & the model (ONNX) from OpenCV Zoo. You can try our scripts (C++ & Python) in opencv_dnn/ with the ONNX model. View the network architecture here.

Please note that OpenCV DNN does not support the latest version of YuNet with dynamic input shape. Please ensure you have the exact same input shape as the one in the ONNX model to run latest YuNet with OpenCV DNN.

examples/detect-image.cpp and examples/detect-camera.cpp show how to use the library.

The library was trained by libfacedetection.train.

How to use the code

You can copy the files in directory src/ into your project, and compile them as the other files in your project. The source code is written in standard C/C++. It should be compiled at any platform which supports C/C++.

Some tips:

• Please add facedetection_export.h file in the position where you copy your facedetectcnn.h files, add #define FACEDETECTION_EXPORT to facedetection_export.h file. See: issues #222
• Please add -O3 to turn on optimizations when you compile the source code using g++.
• Please choose 'Maximize Speed/-O2' when you compile the source code using Microsoft Visual Studio.
• You can enable OpenMP to speedup. But the best solution is to call the detection function in different threads.

You can also compile the source code to a static or dynamic library, and then use it in your project.

How to compile

CNN-based Face Detection on Intel CPU

Using AVX2 instructions

Method	Time	FPS	Time	FPS
	X64	X64	X64	X64
	Single-thread	Single-thread	Multi-thread	Multi-thread
cnn (CPU, 640x480)	50.02ms	19.99	6.55ms	152.65
cnn (CPU, 320x240)	13.09ms	76.39	1.82ms	550.54
cnn (CPU, 160x120)	3.61ms	277.37	0.57ms	1745.13
cnn (CPU, 128x96)	2.11ms	474.60	0.33ms	2994.23

Using AVX512 instructions

Method	Time	FPS	Time	FPS
	X64	X64	X64	X64
	Single-thread	Single-thread	Multi-thread	Multi-thread
cnn (CPU, 640x480)	46.47ms	21.52	6.39ms	156.47
cnn (CPU, 320x240)	12.10ms	82.67	1.67ms	599.31
cnn (CPU, 160x120)	3.37ms	296.47	0.46ms	2155.80
cnn (CPU, 128x96)	1.98ms	504.72	0.31ms	3198.63

• Minimal face size ~10x10
• Intel(R) Core(TM) i7-7820X CPU @ 3.60GHz
• Multi-thread in 16 threads and 16 processors.

CNN-based Face Detection on ARM Linux (Raspberry Pi 4 B)

Method	Time	FPS	Time	FPS
	Single-thread	Single-thread	Multi-thread	Multi-thread
cnn (CPU, 640x480)	404.63ms	2.47	125.47ms	7.97
cnn (CPU, 320x240)	105.73ms	9.46	32.98ms	30.32
cnn (CPU, 160x120)	26.05ms	38.38	7.91ms	126.49
cnn (CPU, 128x96)	15.06ms	66.38	4.50ms	222.28

• Minimal face size ~10x10
• Raspberry Pi 4 B, Broadcom BCM2835, Cortex-A72 (ARMv8) 64-bit SoC @ 1.5GHz
• Multi-thread in 4 threads and 4 processors.

Performance on WIDER Face

Run on default settings: scales=[1.], confidence_threshold=0.02, floating point:

AP_easy=0.887, AP_medium=0.871, AP_hard=0.768

Author

• Shiqi Yu, shiqi.yu@gmail.com

Contributors

All contributors who contribute at github.com are listed here.

The contributors who were not listed at github.com:

• Jia Wu (吴佳)
• Dong Xu (徐栋)
• Shengyin Wu (伍圣寅)

Acknowledgment

The work was partly supported by the Science Foundation of Shenzhen (Grant No. 20170504160426188).

Citation

We published a paper on face detection to evaluate different methods. This project has also been evaluated in the paper.

@article{facedetect-yu,
 author={Yuantao Feng and Shiqi Yu and Hanyang Peng and Yan-ran Li and Jianguo Zhang}
 title={Detect Faces Efficiently: A Survey and Evaluations},
 journal={IEEE Transactions on Biometrics, Behavior, and Identity Science},
 year={2021}
 }

The paper can be open accessed at https://ieeexplore.ieee.org/document/9580485

The loss used in training is EIoU, a novel extended IoU. More details can be found in:

@article{eiou,
 author={Peng, Hanyang and Yu, Shiqi},
 journal={IEEE Transactions on Image Processing}, 
 title={A Systematic IoU-Related Method: Beyond Simplified Regression for Better Localization}, 
 year={2021},
 volume={30},
 pages={5032-5044},
 doi={10.1109/TIP.2021.3077144}
 }

The paper can be open accessed at https://ieeexplore.ieee.org/document/9429909.