SeetaFace2

1 Introduction

The SeetaFace2 face recognition engine includes three core modules required to build a fully automated face recognition system: face detection module FaceDetector, facial key positioning module FaceLandmarker and face feature extraction and Compare the module FaceRecognizer. There are two additional auxiliary modules FaceTracker and QualityAssessor for face tracking and face quality assessment.

SeetaFace2 is developed in standard C++. All modules do not depend on any third-party libraries and support x86 architecture (Windows, Linux) and ARM architecture (Android). The top applications supported by SeetaFace2 include, but are not limited to, face access, insensitivity, face matching, and the like.

SeetaFace2 is a milestone version for face recognition business. The face detection module can achieve more than 92% recall rate under 100 false detection conditions on FDDB. Face key point positioning supports 5 points and 81 points positioning. A face recognition application that supports thousands of people's base libraries than the N module.

Modules	Method Overview	Basic Specifications	Typical Platform Speed
Face Detection	Cascaded CNN	FDDB has a recall rate of 92% (100 false detections). 40 Minimal Face I7: 70FPS(1920x1080) RK3399: 25FPS(640x480)
Facial closure point positioning (81 points and 5 points)	FEC-CNN	Average positioning error (normalized according to the center distance between the two eyes) 0.069 on the 300-W Challenge Set.	I7: 450FPS and 500FPS RK3399: 110FPS and 220FPS
Face feature extraction and comparison	ResNet50	Recognition: In the general 1:N+1 scenario, when the error acceptance rate is 1%, 1000 base library, the preferred recognition rate is over 98%, The 5,000-person base library has a preferred recognition rate of over 95%.	I7: 8FPS RK3399: 2.5FPS

Compared to the 2016 open source SeetaFace 1.0, SeetaFace2 has an order of magnitude improvement in both speed and accuracy.

Version	Face Detection		Key Point Positioning		Face Recognition		Third-party dependencies
Version	Speed [1]	single precision [2]	speed	Features	Training data size	Application	Third-party dependencies
1.0	16FPS	85%	200FPS	5 points	1.4 million sheets	Laboratory	no
2.0	77FPS	92%	500FPS	5/81 points	33 million sheets	Business Environment	no
Remarks	[1] 640x480 input, detection 40x40 face, I7-6700. [2] The accuracy of face detection refers to the recall rate of 100 misunderstood FDDB data sets.

Knowing people to understand everything, open source empowerment and development. SeetaFace2 is committed to the development of AI, and together with industry partners to promote the face recognition technology.

2. Compile

2.1 Compiling dependencies

compilation tool
- For linux
  - GNU Make tool
  - GCC or Clang compiler
- For windows
  - MSVC or MinGW.
- CMake
dependent library
- [Optional] OpneCV Required only when compiling examples
dependency architecture
- CPU supports SSE2 and FMA [optinal] (x86) or NENO (ARM) support

2.2 Compile parameter

BUILD_DETECOTOR: Whether to compile the face detection module. ON: On; OFF: Off
BUILD_LANDMARKER: Whether to compile the face key positioning module. ON: On; OFF: Off
BUILD_RECOGNIZER: Whether to compile the face feature extraction and comparison module. ON: On; OFF: Off
BUILD_EXAMPLE: Whether to compile the example. ON: On; OFF: Off, open requires pre-installation of OpneCV
CMAKE_INSTALL_PREFIX: Installation prefix
SEETA_USE_FMA: Whether use FMA instructions. Default off. Only works in x86 architecture.
SEETA_USE_SSE2: Whether use SSE2 instructions。window and unix default ON，other default OFF。

2.3 Platforms

2.3.1 linux

Dependence
- opencv. Only need to compile the example
  
  sudo apt-get install libopencv-dev

Compile

cd SeetaFace2
mkdir build
cd build
cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=`pwd`/install -DBUILD_EXAMPLE=OFF # Set to ON if there is OpneCV
cmake --build . --config Release

Installation

cmake --build . --config Release --target install/strip

Run the example

Add the directory of the build library to the variable LD_LIBRARY_PATH
```
  export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:`pwd`/bin
```

Copy the model file to the model directory of the program execution directory

  cd SeetaFace2
  cd build
  cd bin
  mkdir model
  cp fd_2_00.dat pd_2_00_pts5.dat pd_2_00_pts81.dat .

Execute the program in the bin directory

points81
```
cd SeetaFace2
cd build
cd bin
./point81
```
search
```
cd SeetaFace2
cd build
cd bin
./search
```

2.3.2 windows

Dependence
- opencv. Only need to compile the example
Compile with the cmake-gui.exe tool. Open cmake-gui.exe

Command line compilation

Add the directory where the cmake command is located to the environment variable PATH

Open "VS2015 Developer Command Prompt" from the Start menu to enter the command line

Compile

cd SeetaFace2
mkdir build
cd build
cmake .. -G"Visual Studio 14 2015" \
      -DCMAKE_INSTALL_PREFIX=install \
      -DCMAKE_BUILD_TYPE=Release \
      -DBUILD_EXAMPLE=OFF # Set to ON if there is OpneCV
#Note: -G: Generators. The generators must match the msvc compiler.
cmake --build . --config Release

Installation

cmake --build . --config Release --target install

Run the example
- Copy the model file to the model directory of the program execution directory
```
  cd SeetaFace2
  cd build
  cd bin
  mkdir model
  cp fd_2_00.dat pd_2_00_pts5.dat pd_2_00_pts81.dat .
```
- Execute the program in the bin directory
  - points81
  - search

2.3.3 Android platform compilation instructions

Install ndk
- Download and install to /home/android-ndk from https://developer.android.com/ndk/downloads
- Setting environment variables
```
export ANDROID_NDK=/home/android-ndk
```

Complie

The host is linux

Compile

  cd SeetaFace2
  mkdir build
  cd build
  cmake .. -DCMAKE_INSTALL_PREFIX=install -DCMAKE_BUILD_TYPE=MinSizeRel -DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK}/build/cmake/android.toolchain.cmake -DANDROID_ABI="armeabi-v7a with NEON" -DANDROID_PLATFORM=android-18 -DBUILD_EXAMPLE=OFF # set ON if OpenCV
  cmake --build . --config MinSizeRel

Installation

  cmake --build . --config MinSizeRel --target install/strip

The host is windows

cmd

Compile

cd SeetaFace2
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=%cd%\install ^
      -G"Unix Makefiles" ^
      -DCMAKE_BUILD_TYPE=MinSizeRel ^
      -DCMAKE_TOOLCHAIN_FILE=%ANDROID_NDK%/build/cmake/android.toolchain.cmake ^
      -DCMAKE_MAKE_PROGRAM=%ANDROID_NDK%/prebuilt/windows-x86_64/bin/make.exe ^
      -DANDROID_ABI=arm64-v8a ^
      -DANDROID_ARM_NEON=ON ^
      -DANDROID_PLATFORM=android-24 ^
      -DBUILD_EXAMPLE=OFF : set ON if OpenCV
cmake --build . --config MinSizeRel

Installation

cmake --build . --config MinSizeRel --target install/strip

msys2 or cygwin

Compile

cd SeetaFace2
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=install -G"Unix Makefiles" -DCMAKE_BUILD_TYPE=MinSizeRel -DCMAKE_TOOLCHAIN_FILE=${ANDROID_NDK}/build/cmake/android.toolchain.cmake  -DCMAKE_MAKE_PROGRAM=${ANDROID_NDK}\prebuilt\windows-x86_64\bin\make.exe -DANDROID_ABI=arm64-v8a -DANDROID_ARM_NEON=ON -DBUILD_EXAMPLE=OFF # set ON if OpenCV
cmake --build . --config MinSizeRel

Installation

cmake --build . --config MinSizeRel --target install/strip

Parameter Description: https://developer.android.google.cn/ndk/guides/cmake
- ANDROID_ABI: The following values can be taken: Goal ABI. If the target ABI is not specified, CMake uses armeabi-v7a by default. Valid ABI are:
  - armeabi：CPU with software floating point arithmetic based on ARMv5TE
  - armeabi-v7a：ARMv7-based device with hardware FPU instructions (VFP v3 D16)
  - armeabi-v7a with NEON：Same as armeabi-v7a, but with NEON floating point instructions enabled. This is equivalent to setting -DANDROID_ABI=armeabi-v7a and -DANDROID_ARM_NEON=ON.
  - arm64-v8a：ARMv8 AArch64 Instruction Set
  - x86：IA-32 Instruction Set
  - x86_64 - x86-64 Instruction Set
- ANDROID_NDK The path of installed ndk in host
- ANDROID_PLATFORM: For a full list of platform names and corresponding Android system images, see the [Android NDK Native API] (https://developer.android.google.com/ndk/guides/stable_apis.html)
- ANDROID_ARM_MODE
- ANDROID_ARM_NEON
- ANDROID_STL:Specifies the STL that CMake should use.
  - c++_shared: use libc++ shared library
  - c++_static: use libc++ static library
  - none: no stl
  - system: use system STL

2.3.4 IOS platfrom

example IOS device.

Dependence
- One PC within MacOS.
- Source code from git
Command lines
- Use cmake compile and install
```
cd SeetaFace2
mkdir build
cd build
chmod +x ../ios/cmake.sh
../ios/cmake.sh -DCMAKE_INSTALL_PREFIX=`pwd`/install
make -j4
make install
```
  After all commands above succeed, libraries would install into SeetaFace2/build/install.
- Compile for simlulator. Change cmake parameters like: ../ios/cmake.sh -DIOS_PLATFORM=SIMULATOR64 -DPLATFORM=x64
- See <root>/ios/cmake.sh and <root>/ios/iOS.cmake for more compilation controls.

3. Directory structure

|-- SeetaFace2  
    |-- documents (SDK interface documentation)  
    |-- example(C++ version SDK sample code)  
    |-- FaceDetector  
    |-- FaceLandmarker(Feature Point Positioning Module)  
    |-- FaceRecognizer (Face Feature Extraction and Alignment Module)  
    |-- SeetaNet (forward computing framework module)

4. Model download

Face Detection Module FaceDetector Model Download Link:
MD5 : E88669E5F1301CA56162DE8AEF1FD5D5
Baidu network disk: https://pan.baidu.com/s/1Dt0M6LXeSe4a0Pjyz5ifkg Extraction code: fs8r
Dropbox : https://www.dropbox.com/s/cemt9fl48t5igfh/fd_2_00.dat?dl=0
Facial feature 5-point positioning module FaceLandmarker model download link:
MD5: 877A44AA6F07CB3064AD2828F50F261A
Baidu network disk: https://pan.baidu.com/s/1MqofXbmTv8MIxnZTDt3h5A Extraction code: 7861
Dropbox : https://www.dropbox.com/s/noy8tien1gmw165/pd_2_00_pts5.dat?dl=0
Face feature 81 point positioning module FaceLandmarker model download link:
MD5 : F3F812F01121B5A80384AF3C35211BDD
Baidu network disk: https://pan.baidu.com/s/1CCfTGaSg_JSY3cN-R1Myaw Extraction code: p8mc
Dropbox : https://www.dropbox.com/s/v41lmclaxpwow1d/pd_2_00_pts81.dat?dl=0
Facial feature extraction and comparison module FaceRecognizer model download link:
MD5 : 2D637AAD8B1B7AE62154A877EC291C99
Baidu network disk: https://pan.baidu.com/s/1y2vh_BHtYftR24V4xwAVWg Extraction code: pim2
Dropbox : https://www.dropbox.com/s/6aslqcokpljha5j/fr_2_10.dat?dl=0

5. example

5.1 This project comes with examples

The example/search/example.cpp example shows a simple and complete process for face recognition, including:

Pre-registered images in the face-to-face recognition base library (the default registration in the example is "1" .jpg "Face in the face";
Turn on the camera to detect the face in the camera screen; 3. Identify the detected face and determine the identity of the face.

If the tester wants to successfully identify his face in the bottom library, he needs to add the image named after his own name (name + .jpg) in the bottom register registration list of example.cpp, and copy the image file named by his own name. Go to the program's running directory, recompile example and run the program to test the recognition effect.

5.2 Other projects that have used this project

FaceRecognizer: https://github.com/KangLin/FaceRecognizer
SeetaFace2AndroidDemo: https://github.com/xiaoxiaoazhang/SeetaFace2AndroidDemo

6. Developer Community

Developers are welcome to join the SeetaFace developer community, please add SeetaFace assistant helper WeChat, after review, invite to join the group.

6.1 Code Contribution

Developers are welcome to contribute quality code, and all developer code needs to be submitted in the develop branch.

7. Business cooperation

If you want to purchase the SeetaFace commercial version engine for more accurate and faster face recognition algorithms or more for face verification, expression recognition, heart rate estimation, attitude estimation, line-of-sight tracking, etc., please contact Business Email bd@seetatech.com.

8. Open source agreement

SeetaFace2 is open source according to [BSD 2-Clause license] (LICENSE).

Gitee 极速下载 / SeetaFace2

SeetaFace2

1 Introduction

2. Compile

2.1 Compiling dependencies

2.2 Compile parameter

2.3 Platforms

2.3.1 linux

2.3.2 windows

2.3.3 Android platform compilation instructions

2.3.4 IOS platfrom

3. Directory structure

4. Model download

5. example

5.1 This project comes with examples

5.2 Other projects that have used this project

6. Developer Community

6.1 Code Contribution

7. Business cooperation

8. Open source agreement

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Gitee 极速下载 / SeetaFace2 .gitee-modal { width: 500px !important; }

SeetaFace2

1 Introduction

2. Compile

2.1 Compiling dependencies

2.2 Compile parameter

2.3 Platforms

2.3.1 linux

2.3.2 windows

2.3.3 Android platform compilation instructions

2.3.4 IOS platfrom

3. Directory structure

4. Model download

5. example

5.1 This project comes with examples

5.2 Other projects that have used this project

6. Developer Community

6.1 Code Contribution

7. Business cooperation

8. Open source agreement

Comments ( 0 )

About

Releases

Contributors

Activities

Search

Gitee 极速下载 / SeetaFace2