antispoofing.lbp 2.0.2

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 calculate the feature vectors for each frame separately (and save them into a single file for the full video), you have to run:

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

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. 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/cmphistmodels.py -s replay

Do not forget the -s option if you want the scores for each video saved in a file.

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. 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 -s replay

Do not forget the -s option if you want the scores for each video saved in a file.

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. 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 --eval -s replay

Do not forget the -s option if you want the scores for each video saved in a file.

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

Classification with support vector machine (SVM) on a different database or database subset

In the training process, the SVM machine, as well as the normalization and PCA parameters are saved in an .hdf5 file. They can be used later for classification of data from a different database or database subset. This can be done using the script ./bin/svmtrain_lbp.py. The default input and output directories are ./lbp_features and ./res. To execute the script, call:

$ ./bin/svmeval_lbp.py replay

Do not forget the -s option if you want the scores for each video saved in a file. Also, do not forget to specify the right .hdf5 file where the SVM machine and the parameters are saved using the -i parameter (the default one is ./res/svm_machine.hdf5

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

Reproduce paper results

The exact commands to reproduce the results from the paper are given here. First, feature exatraction should be done as follows:

$ ./bin/calcframelbp.py -d features/regular replay
$ ./bin/calcframelbp.py -d features/transitional replay
$ ./bin/calcframelbp.py -d features/direction_coded replay
$ ./bin/calcframelbp.py -d features/modified replay
$ ./bin/calcframelbp.py -d features/per-block -b 3 replay

The results in Table II are obtained with the following commands:

$ ./bin/mkhistmodel.py -v features/regular -d models/regular replay
$ ./bin/cmphistmodels.py -v features/regular -m models/regular -d scores/regular -s replay

By changing the -v parameter, you can change the type of features, resulting in the scores for the different columns of the table.

The results in Table III are obtained by the same commands, using the corresponding value for the -v parameter for the per-block computed feature.

The results in Table IV for LDA and SVM classification are obtained by the following two commands, respectively:

$ ./bin/ldatrain_lbp.py -v features/regular -d scores/regular -n replay
$ ./bin/svmtrain_lbp.py -v features/regular -d scores/regular -n -r replay

The results for the CASIA-FASD database can be obtained in the same way, by specifying the casia parameter at the end of the commands. Note that the results for CASIA-FASD are reported on per-block basis, and using 5-fold cross validation. This means that the results need to be generated 5 times, training with different fold, which can be specified as an argument as well.

Important note: the results in the last column of Table V are not straight-forwardly reproducible at the moment (in particular, the concatenation of histograms is not directly supported using the scripts in this satellite package). Furthermore, at the present state, the scripts do not support the NUAA database. Work to solve this incovenience is in progress :)