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An open source library for face detection in images. The face detection speed can reach 1000FPS.

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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 file has also been provided in directory ./models/.

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 facedetectionexport.h file in the position where you copy your facedetectcnn.h files, add #define FACEDETECTIONEXPORT 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

| Method |Time | FPS |Time | FPS | |--------------------|--------------|-------------|--------------|-------------| | | X64 |X64 | X64 |X64 | | |Single-thread |Single-thread|Multi-thread |Multi-thread | |cnn (CPU, 640x480) | 58.06ms. | 17.22 | 12.93ms | 77.34 | |cnn (CPU, 320x240) | 13.77ms | 72.60 | 3.19ms | 313.14 | |cnn (CPU, 160x120) | 3.26ms | 306.81 | 0.77ms | 1293.99 | |cnn (CPU, 128x96) | 1.41ms | 711.69 | 0.49ms | 2027.74 |

  • Minimal face size ~10x10
  • Intel(R) Core(TM) i7-1065G7 CPU @ 1.3GHz

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) | 492.99ms | 2.03 | 149.66ms | 6.68 | |cnn (CPU, 320x240) | 116.43ms | 8.59 | 34.19ms | 29.25 | |cnn (CPU, 160x120) | 27.91ms | 35.83 | 8.43ms | 118.64 | |cnn (CPU, 128x96) | 17.94ms | 55.74 | 5.24ms | 190.82 |

  • Minimal face size ~10x10
  • Raspberry Pi 4 B, Broadcom BCM2835, Cortex-A72 (ARMv8) 64-bit SoC @ 1.5GHz

Performance on WIDER Face

Run on default settings: scales=[1.], confidencethreshold=0.3, floating point: ``` APeasy=0.856, APmedium=0.842, APhard=0.727 ```



All contributors who contribute at are listed here.

The contributors who were not listed at * Jia Wu (吴佳) * Dong Xu (徐栋) * Shengyin Wu (伍圣寅)


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


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

 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={to appear}

@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} }

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