maskattack.lbp 1.0.0

Texture (LBP) based counter-measures for the 3D MASK ATTACK database

Using an automatic installer

Using pip is the easiest (shell commands are marked with a $ signal):

$ pip install maskattack.lbp

You can also do the same with easy_install:

$ easy_install maskattack.lbp

This will download and install this package plus any other required dependencies. It will also verify if the version of Bob you have installed is compatible.

This scheme works well with virtual environments by virtualenv or if you have root access to your machine. Otherwise, we recommend you use the next option.

Using zc.buildout

Download the latest version of this package from PyPI and unpack it in your working area. The installation of the toolkit itself uses buildout. You don't need to understand its inner workings to use this package. Here is a recipe to get you started:

$ python bootstrap.py
$ ./bin/buildout

These 2 commands should download and install all non-installed dependencies and get you a fully operational test and development environment.

[external]
recipe = xbob.buildout:external
egg-directories=/Users/crazyfox/work/bob/build/lib

Calculate the LBP features

The first stage of the process is calculating the feature vectors, which are essentially normalized LBP histograms. A single feature vector for each frame of the video (both for the depth and color images) is computed and saved as a multiple row array in a single file.

The program to be used for this is ./bin/calclbp.py. It uses the utility script spoof/calclbp.py. Depending on the command line arguments, it can compute different types of LBP histograms over the normalized face bounding box. Cropped and normalized images can be saved to a folder (./img_cropped by default) and used in future computations to skip cropping using -sc flag.

Furthermore, the normalized face-bounding box can be divided into blocks or not.

The following commands will calculate the feature vectors of all the videos in the database and will put the resulting .hdf5 files with the extracted feature vectors in the output directory ./lbp_features/r_1 with and without skipping the cropping step:

$ bin/calclbp.py -ld ./lbp_features/r_1 --el regular

$ bin/calclbp.py -cd ./img_cropped -ld ./lbp_features/r_1 --el regular -sc

In the above command, the program will crop (64x64 by default) and normalize the images according to the eye positions available in the database. The cropped images will be saved to the default directory (img_cropped) which can be changed using --cropped-dir argument.

To see all the options for the script calclbp.py, just type --help at the command line. Change the default option in order to obtain various features described in the paper.

Classification using Chi-2 distance

The clasification using Chi-2 distance consists of two steps. The first one is creating the histogram model (average LBP histogram of all the real access videos in the training set). The second step is comparison of the features of development and test videos to the model histogram and writing the results.

The script for performing Chi-2 histogram comparison is ./bin/cmphistmodels.py. It expects that the LBP features of the videos are stored in a folder ./bin/lbp_features.

First, it calculates the average real-access histogram using the training set and next, it computes the distances and writes the results in a file using the utility script spoof/chi2.py`. The default input directory is ``./lbp_features, while the default output directory is ./res. To execute this script on previously extracted features, just run:

$ ./bin/cmphistmodels.py -v ./lbp_features/r_1

To see all the options for the script cmphistmodels.py, just type --help at the command line.

Classification with linear discriminant analysis (LDA)

The classification with LDA is performed using the script ./bin/ldatrain_lbp.py. It makes use of the scripts ml/lda.py, ml/pca.py (if PCA reduction is performed on the data) and ml/norm.py (if the data need to be normalized). The default input and output directories are ./lbp_features and ./res. To execute the script with prior PCA dimensionality reduction as is done in the paper, call:

$ ./bin/ldatrain_lbp.py -v ./lbp_features/r_1 -r

To see all the options for this script, just type --help at the command line.

Classification with support vector machine (SVM)

The classification with SVM is performed using the script ./bin/svmtrain_lbp.py. It makes use of the scripts ml/pca.py (if PCA reduction is performed on the data) and ml\norm.py (if the data need to be normalized). The default input and output directories are ./lbp_features and ./res. To execute the script as is done in the paper, call:

$ ./bin/svmtrain_lbp.py -v ./lbp_features/r_1

To see all the options for this script, just type --help at the command line.

Plotting the HTER

The classification scripts cmphistmodels, ldatrain_lbp and svmtrain_lbp, run the experiments in 1000-fold manner, by randomly assigning subject ids to training, development and test sets (of fixed size). The resulting files are written to the res folder by default. If all the experiments in the paper are run (3 classification, 4 LBP methods with and without blocks - see run_experiments.sh), the barplot.py script will create and save the image with HTER bar plot (under the res folder), for which the height shows the mean of the HTER values in 1000 folds and the error bar shows the standard deviation:

$ ./bin/barplot.py