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.

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Dependency:

• Vlfeat library: http://www.vlfeat.org/

• libLinear: www.csie.ntu.edu.tw/~cjlin/liblinear/

Datasets in use:

[300-W] http://ibug.doc.ic.ac.uk/resources/facial-point-annotations/

How to use:

1. 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

   For example:

   options.trainingImageDataPath = './data/lfpw/trainset/';

   options.trainingTruthDataPath = './data/lfpw/trainset/';

   options.testingImageDataPath = './data/lfpw/testset/';

   options.testingTruthDataPath = './data/lfpw/testset/';

2. 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

   libLinear:

   Vlfeat:

• 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

• Open Matlab

• Go to i.e. lib/liblinear-1.96/matlab/ in Matlab editor.

• Run make.m to comile *.mex files.

3. 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;

4. Do training:

   run_training();

5. Do testing:

   do_testing();

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.