This project is aimed to reproducing (partially) the face alignment algorithm in the CVPR 2014 paper:
Face Alignment at 3000 FPS via Regressing Local Binary Features. Shaoqing Ren, Xudong Cao, Yichen Wei, Jian Sun; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014, pp. 1685-1692
First of all, we need prepare datasets, such as afw, lfpw, helen, ibug, etc. All these can be downloaded freely from https://ibug.doc.ic.ac.uk/resources/facial-point-annotations. Then get the filelist file Path_Images.txt for each dataset (please refer to the Q&A).
For training, initialize variable dbnames as {'Dataset_a', 'Dataset_b', ..., }, then run train_model in command line window.
For testing, run test_model in command line window after having obtained trained model. Please remember to initialize dbnames to be the names of dataset you would like to test on.
Off-the-shelf model can be downloaded here: http://pan.baidu.com/s/1i325Rbn, whose configure file can be found in folder "models". Its performance is analogy to the lbf_fast model evaluated in the original paper.
How to get the file Path_Images.txt?
It can be obtained by run bat file in the root folder of a dataset, the code is simply "dir /b/s/p/w *.jpg>Path_Images.txt".
What is Ts_bbox.mat?
This problem is solved in recent version. Ts_bbox is a transformation matrix to adapt bounding boxes obtained from face detector to the boxes suitable for the face alignment algorithm.
How to define the variable dbnames in train_model and test_model functions?
It is formed as a cell array {'dbname_1' 'dbname_2' ... 'dbname_N'}. For example, if we use the images in afw for trainig, we then define it as {'afw'}.
Why does an error occur when initializing parallel computing?
It may be caused by Matlab version. For Matlab 2014, it will be okay. For earlier version, please use matlabpool alternatively.
Some function correspondences from Matlab 2014 to older version
fitgeotrans -> cp2tform, transformPointsForward -> tformfwd
For those Tencent QQ users, you can join the group face hacker (180634020) to discuss more on facial algorithms. Along with the request, it is needed to provide your affiliation (university/company where you are studying/working).