https://github.com/yulequan/face-alignment-in-3000fps

a c++ implementation of face alignment in 3000fps
https://github.com/yulequan/face-alignment-in-3000fps

Last synced: about 2 months ago
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a c++ implementation of face alignment in 3000fps

• Host: GitHub
• URL: https://github.com/yulequan/face-alignment-in-3000fps
• Owner: yulequan
• License: other
• Created: 2015-03-06T07:41:31.000Z (over 9 years ago)
• Default Branch: master
• Last Pushed: 2019-04-22T00:27:04.000Z (over 5 years ago)
• Last Synced: 2024-04-06T03:35:08.302Z (6 months ago)
• Language: C++
• Homepage:
• Size: 1.42 MB
• Stars: 376
• Watchers: 34
• Forks: 234
• Open Issues: 18
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Face-alignment-in-3000fps

This project is a C++ reimplementation of face alignment in 3000fps in the CVPR 2014 paper:

[ Face Alignment at 3000 FPS via Regressing Local Binary Features. ]().

### Update openMP support !!!

 I modify my code to support openMP. You can use it in GCC(Linux) or in VS (Windows).

 

 If you use it in Linux, you should comment or uncomment `FIND_PACKAGE( OpenMP REQUIRED)`  in CmakeLists.txt.

 

 If you use it in Windows, you can directly use it. 

 

### VS project 

 I add a VS project. 

 

### Usage

1. Download datasets and get Path_Images.txt as [jwyang/face-alignment](https://github.com/jwyang/face-alignment). 

2. To compiler the program: go to folder `build` and 

   

   cmake .

  

   make

3. To train a new model: set global parameters, model path, train database name in `LBF.cpp`. Use `"LBF.out TrainModel"`.

4. To test a model on dataset: set model path, test dataset name in `LBF.cpp`. Use `"LBF.out TestModel"`.

###Model

I have trained a model on AFW, HELEN,LFPW dataset. You can download it from [here](https://pan.baidu.com/s/1jHNXa8A

) or [google drive](https://drive.google.com/folderview?id=0ByeDfKY7bL0_fmg2RWN2V0xtQ19veW1wdFVJRjBaRHBuUmJNNERHc0YyQ2lLVXJodDZTbk0&usp=sharing). 

### FAQ

* How to get the bounding box of image ?

	I use the face detector in OpenCV to get the bounding box.You can use any detector to get the bounding box but you must provide a bounding box of similar measure with the training data. 

* How about the liblinear?

	I add the liblinear source code as the project code. So you can directly compiler this project and don't need to consider to compiler this library.

### Contact 

If you have any question, you can create an `issue` on GitHub.

Or you can email [email protected]

### Reference Project

* [soundsilence/FaceAlignment](https://github.com/soundsilence/FaceAlignment)

* [jwyang/face-alignment](https://github.com/jwyang/face-alignment)