This is an open source library for CNN-based face detection in images. The CNN model has been converted to stastic 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.
- 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.
| 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.
| 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
- Shiqi Yu, [email protected]
- Jia Wu
- Shengyin Wu
- Dong Xu
The work is partly supported by the Science Foundation of Shenzhen (Grant No. JCYJ20150324141711699).