antispoofing.motion 1.0.2

Using an automatic installer

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

$ pip install antispoofing.motion

You can also do the same with easy_install:

$ easy_install antispoofing.motion

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 Frame Differences

The first stage of the process is to calculate the normalized frame differences using video sequences. The program that will do that should be sitting in bin/framediff.py. It can calculate normalize frame differences in distinct parts of the scene (given you provide face locations for each of the frames in all video sequences to be analyzed).

To execute the frame difference process to all videos in the PRINT-ATTACK database, just execute:

$ ./bin/framediff.py

There are more options for the framediff.py script you can use. Just type –help at the command line for instructions.

Calculate the 5 Quantities

The second step in calculating the frame differences is to compute the set of 5 quantities that are required for the detection process. To reproduce the results in the paper, we accumulate the results in windows of 20 frames, without overlap:

$ ./bin/diffcluster.py

There are more options for the diffcluster.py script you can use. Just type –help at the command line for instructions.

Training an MLP

Training MLPs to perform discrimination should go like this:

$ ./bin/rproptrain.py --verbose --epoch=10000 --batch-size=500 --no-improvements=1000000 --maximum-iterations=10000000

This will create a new MLP and train it using the data produced by the “clustering” step. The training can take anywhere from 20 to 30 minutes (or even more), depending on your machine speed. You should see some debugging output with the partial results as the training go along:

...
iteration: RMSE:real/RMSE:attack (EER:%) ( train | devel )
0: 9.1601e-01/1.0962e+00 (60.34%) | 9.1466e-01/1.0972e+00 (58.71%)
0: Saving best network so far with average devel. RMSE = 1.0059e+00
0: New valley stop threshold set to 1.2574e+00
10000: 5.6706e-01/4.2730e-01 (8.29%) | 7.6343e-01/4.3836e-01 (11.90%)
10000: Saving best network so far with average devel. RMSE = 6.0089e-01
10000: New valley stop threshold set to 7.5112e-01
20000: 5.6752e-01/4.2222e-01 (8.21%) | 7.6444e-01/4.3515e-01 (12.07%)
20000: Saving best network so far with average devel. RMSE = 5.9979e-01
20000: New valley stop threshold set to 7.4974e-01

The resulting MLP will be saved in the default output directory called window_based. The resulting directory will also contain performance analysis plots. The results derived after this step are equivalent to the results shown at Table 2 and Figure 3 at the paper.

To get results for specific supports as shown at the first two lines of Table 2, just select the support using the --support=hand or --support=fixed as a flag to rproptrain.py. At this point, it is adviseable to use different output directories using the --output-dir flag as well. If you need to modify or regenerate Figure 3 at the paper, just look at antispoofing/ml/perf.py, which contains all plotting and analysis routines.

Running the Time Analysis

The time analysis is the end of the processing chain, it fuses the scores of instantaneous MLP outputs to give out a better estimation of attacks and real-accesses. To use it:

$ ./bin/time_analysis.py --network-dir=window_based --feature-dir=clustered --support=hand+fixed

The 3 curves on Figure 4 at the paper relate to the different support types. Just repeat the procedure for every system trained with data for a particular support (equivalent for then entries in Table 2). The output for this script is dumped in PDF (plot) and text (.rst file) on the directory containing the matching neural net (passed as parameter to --network-dir).