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.

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, [email protected]

Contributors

• Jia Wu
• Shengyin Wu
• Dong Xu

Acknowledgment

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