antispoofing.lbp 1.1.2

Using an automatic installer

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

$ pip install antispoofing.lbp

You can also do the same with easy_install:

$ easy_install antispoofing.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.

Calculate the LBP features

The first stage of the process is calculating the feature vectors, which are essentially normalized LBP histograms. There are two types of feature vectors:

1. per-video averaged feature-vectors (the normalized LBP histograms for each frame, averaged over all the frames of the video. The result is a single feature vector for the whole video), or

2. a single feature vector for each frame of the video (saved as a multiple row array in a single file).

The program to be used for the first case is ./bin/calclbp.py, and for the second case ./bin/calcframelbp.py. They both use the utility script spoof/calclbp.py. Depending on the command line arguments, they can compute different types of LBP histograms over the normalized face bounding box. Furthermore, the normalized face-bounding box can be divided into blocks or not.

The following command will calculate the per-video averaged feature vectors of all the videos in the REPLAY-ATTACK database and will put the resulting .hdf5 files with the extracted feature vectors in the default output directory ./lbp_features:

$ ./bin/calclbp.py --ff 50 replay

In the above command, the face size filter is set to 50 pixels (as in the paper), and the program will discard all the frames with detected faces smaller then 50 pixels as invalid.

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

If you want to see all the options for a specific database (e.g. protocols, lighting conditions etc.), type the following command (for Replay-Attack):

$ ./bin/calclbp.py replay --help

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 to use for creating the histogram model is ./bin/mkhistmodel.py. It expects that the LBP features of the videos are stored in a folder ./bin/lbp_features. The model histogram will be written in the default output folder ./res. You can change this default features by setting the input arguments. To execute this script fro Replay-Attack, just run:

$ ./bin/mkhistmodel.py replay

The script for performing Chi-2 histogram comparison is ./bin/cmphistmodels.py, and it assumes that the model histogram has been already created. It makes use of the utility script spoof/chi2.py and ml/perf.py for writing the results in a file. The default input directory is ./lbp_features, while the default input directoru for the histogram model as well as default output directory is ./res. To execute this script for Replay-Attack, just run:

$ ./bin/cmphistmodel.py replay

To see all the options for the scripts mkhistmodel.py and 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 (for Replay-Attack), call:

$ ./bin/ldatrain_lbp.py -r replay

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 with prior normalization of the data in the range [-1, 1] as in the paper (for Replay-Attack), call:

$ ./bin/svmtrain_lbp.py -n replay

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