Matlab Implementation of Supervised Descent Method
A simple Matlab implementation of Supervised Descent Method (SDM) for Face Alignment.
I provide both training and testing modules and one trained model of LFPW subset of 300-W dataset.
You can find the ogirinal paper of my implementation:
Xiong et F. De la Torre, Supervised Descent Method and its Applications to Face Alignment, CVPR 2013.
Datasets in use:
How to use:
Download 300-W data (i.e. LFPW) from above link and put into "./data" folder, then correct the dataset path to your dataset foler in setup.m
mkdir -p data
options.trainingImageDataPath = './data/lfpw/trainset/';
options.trainingTruthDataPath = './data/lfpw/trainset/';
options.testingImageDataPath = './data/lfpw/testset/';
options.testingTruthDataPath = './data/lfpw/testset/';
Download and install dependencies: libLinear, Vlfeat, mexopencv, put into "./lib" folder and compile if necessary. Make sure you already addpath(...) all folders in matlab. Check and correct the library path in setup.m.
mkdir -p lib
- Open Matlab
- Go to i.e. lib/liblinear-1.96/matlab/ in Matlab editor.
- Run make.m to comile *.mex files.
cd lib/vlfeat/ && make
- cd ./toolbox in Matlab editor.
- Run vl_setup
- Compile mex Hog functions:
cd misc mex -L../../bin/glnx86 -lvl -I../ -I../../ vl_hog.c
- Setup libvl.so path.
- Assume that your libvl.so located at: <vlfeat_folder>/bin/glnx86
Create soft link:
ln -s <vlfeat_folder>/bin/glnx86/libvl.so /usr/local/libvl.so Check if the libvl.so is ready to use. ldd vl_hog.mexglx If libvl.so still not found. Add /usr/local/lib into /etc/ld.so.conf (sudo). sudo ldconfig ldconfig -p | grep libvl.so Check again: >> ldd vl_hog.mexglx
If you run first time. You should set these following parameters to learn shape and variation. For later time, reset to 0.
options.learningShape = 1; options.learningVariation = 1;
Note: in the program, we provide training models of LFPW (68 landmarks) in folder: "./model". The program does not optimize speed and memory during training, the memory problem may happens if you train on too much data.