libfacedetection

This is an open source library for CNN-based face detection in images. The CNN model has been converted to static variales 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 speedup 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.

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

• Shiqi Yu, shiqi.yu@gmail.com

Contributors

• Jia Wu
• Shengyin Wu
• Dong Xu

Acknowledgment

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