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

examples/libfacedetectcnn-example.cpp shows how to use the library.

Examples

How to Compile

  • 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.

Create a folder build

mkdir -p build; cd build; rm -rf *;

Cross build for aarch64

  1. set cross compiler for aarch64 (please refer to aarch64-toolchain.cmake)
  2. set opencv path since the example code depends on opencv
cmake \
    -DENABLE_INT8=ON \
    -DENABLE_NEON=ON \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DCMAKE_TOOLCHAIN_FILE=../aarch64-toolchain.cmake \
     ..

make

Native build for avx2

cmake \
    -DENABLE_INT8=ON \
    -DENABLE_AVX2=ON \
    -DCMAKE_BUILD_TYPE=RELEASE \
    -DDEMO=ON \
     ..

make

CNN-based Face Detection on Windows

Method Time FPS Time FPS
X64 X64 X64 X64
Single-thread Single-thread Multi-thread Multi-thread
OpenCV Haar+AdaBoost (640x480) -- -- 12.33ms 81.1
cnn (CPU, 640x480) 64.21ms 15.57 15.59ms 64.16
cnn (CPU, 320x240) 15.23ms 65.68 3.99ms 250.40
cnn (CPU, 160x120) 3.47ms 288.08 0.95ms 1052.20
cnn (CPU, 128x96) 2.35ms 425.95 0.64ms 1562.10
  • OpenCV Haar+AdaBoost runs with minimal face size 48x48
  • Face detection only, and no landmark detection included.
  • Minimal face size ~12x12
  • Intel(R) Core(TM) i7-7700 CPU @ 3.6GHz.

CNN-based Face Detection on ARM Linux (Raspberry Pi 3 B+)

Method Time FPS Time FPS
Single-thread Single-thread Multi-thread Multi-thread
cnn (CPU, 640x480) 512.04ms 1.95 174.89ms 5.72
cnn (CPU, 320x240) 123.47ms 8.10 42.13ms 23.74
cnn (CPU, 160x120) 27.42ms 36.47 9.75ms 102.58
cnn (CPU, 128x96) 17.78ms 56.24 6.12ms 163.50
  • Face detection only, and no landmark detection included.
  • Minimal face size ~12x12
  • Raspberry Pi 3 B+, Broadcom BCM2837B0, Cortex-A53 (ARMv8) 64-bit SoC @ 1.4GHz

Author

Contributors

  • Jia Wu
  • Shengyin Wu
  • Dong Xu

Acknowledgment

The work is partly supported by the Science Foundation of Shenzhen (Grant No. JCYJ20150324141711699).