/libfacedetection

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

Primary LanguageC++OtherNOASSERTION

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.

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

The library was trained by libfacedetection.train.

Examples

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

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.], confidence_threshold=0.3, floating point:

AP_easy=0.856, AP_medium=0.842, AP_hard=0.727

Author

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.