Package: bob.paper.ICB2015
This package provides the source code to run the experiments published in the paperOn the Vulnerability of Palm Vein Recognition to Spoofing Attacks.
It relies on thePalmveinRecLibto execute the palmvein recognition experiments, and some satellite packages fromBobto compute the evaluation.NoteCurrently, this package only works in Unix-like environments and under MacOS.
Due to limitations of theBoblibrary, MS Windows operating systems are not supported.
We are working on a port ofBobfor MS Windows, but it might take a while.
In the meanwhile you could use ourVirtualBoximages that can be downloadedhere.InstallationThe installation of this package relies on theBuildOutsystem. By default, the command line sequence:$ ./python bootstrap-buildout.py
$ ./bin/buildoutshould download and install all requirements, including thePalmveinRecLib, the database interfacebob.db.verapalm, the Spoofing evaluation frameworkantispoofing.evaluationand all their required packages.
There are a few exceptions, which are not automatically downloaded:BobThe face recognition experiments rely on the open source signal-processing and machine learning toolboxBob.
To installBob, please visithttp://www.idiap.ch/software/boband follow the installation instructions.
Please verify that you have at least version 2.0 of Bob installed.
If you have installed Bob in a non-standard directory, please open the buildout.cfg file from the base directory and set theprefixesdirectory accordingly.NoteThe experiments that we report in thePaperwere generated withBobversion 2.0 andPalmveinRecLibversion 2.0.0.
If you use different versions of either of these packages, the results might differ slightly.
For example, we are aware that, due to some initialization differences, the results using Bob 1.2.0 and 1.2.1 are not identical, but similar.Image DatabasesThe experiments are run on external image databases.
We do not provide the images from the databases themselves.
Hence, please contact the database owners to obtain a copy of the images.
The two databases used in our experiments can be downloaded here:VERA Palmvein [vera]:http://www.idiap.ch/dataset/vera-palmveinVERA SpoofingPalmvein [vera]:http://www.idiap.ch/dataset/vera-spoofingpalmveinNoteFor the experiments you have to create a unique directory (example: vera) and two subdirectories (vera/Palmvein) and (vera/SpoofingPalmvein) that contain the databases.
After downloading the databases, you will need to tell our software, where it can find them by changing theconfiguration files.
In particular, please update the--imagedirto indicate the directory of the images inbob.db.verapalm.Please let all other configuration parameters unchanged as this might influence the face recognition experiments and, hence, the reproducibility of the results.Getting helpIn case anything goes wrong, please feel free to open a new ticket in ourGitLabpage, or send an email topedro.tome@idiap.ch.Recreating the results of thePaperAfter successfully setting up the databases, you are now able to run the palmvein recognition and spoofing attack experiments as explained in thePaper.The experiment configurationThe palmvein recognition experiment are run using thebob.palmveinpackage, but for convenience there exists a wrapper script that set up the right parametrization for the call to thePalmveinRecLib.
The configuration files that are used by thePalmveinRecLib, which contain all the parameters of the experiments, can be found in thebob.palmvein/bob/palmvein/configurationsdirectory.Running the experimentsThis script can be found inbin/icb2015_palmvein_NOMandSpoofingAttack.py.
It requires some command line options, which you can list using./bin/icb2015_palmvein_NOMandSpoofingAttack.py--help.
Usually, the command line options have a long version (starting with--) and a shortcut (starting with a single-), here we use only the long versions:--database: Specify the name of the databases to run experiments on. (default:verapalm).--preprocessing: Specify the image preprocessing to run; the preprocessors will automatically assigned to the according experiment. Possible value ispalmvein-preprocessor.--features: Specify the features to run the experimetns. By default, the feature selected is the ‘lbp-linearbinarypatterns’ local binary patterns - LBP.--tool: Specify the matcher to run the experimetns. By default, the match-lbp is selected.--protocols: Specify a list of protocols that you want to run. Possible values areNOM50andSpoofingAttack50. By default, all protocols are used.--dry-run: Use this option to print the calls to thePalmveinRecLibwithout executing them.--temp-directory: Specify a directory where temporary files will be stored (default:temp). This directory can be deleted after all experiments ran successfully.--result-directory: Specify a directory where final result files will be stored (default:results). This directory is required to evaluate the experiments.Additionally, you can pass options directly to thePalmveinRecLib, but you should do that with care.
Simply use--to separate options to thebin/icb2015_palmvein_NOMandSpoofingAttack.pyfrom options to thePalmveinRecLib.
For example, the--forceoption might be of interest.
See./bin/palmveinverify.py--helpfor a complete list of options.It is advisable to use the--dry-runoption before actually running the experiments, just to see that everything is correct.
Also, the Info (2) verbosity level prints useful information, e.g., by adding the--verbose--verbose(or shortly-vv) on the command line.
A commonly used command line sequence to execute the face recognition algorithm on both databases could be:Run the experiments on the VERA Palm database:$ ./bin/icb2015_palmvein_NOMandSpoofingAttack.py -vv --databases verapalmNoteAll output directories of the scripts will be automatically generated if they do not exist yet.WarningThe execution of the script may take a long time and require large amounts of memory.
Nevertheless, the scripts are set up such that they re-use all parts of the experiments as far as this is possible.Evaluating the experimentsAfter all experiments have finished successfully, the resulting score files can be evaluated.
For this, thebin/icb2015_evaluate.pyscript can be used to create a pdf file with the SFAR curves that are provided in the paper. See./bin/plot_on_demand.py--helpfor a complete list of options. To replicate the results of the paper:--dev-file: Specify the file of the scores file (4-column) containing the scores for the verification system, Normal Operation Mode (NOM) scenario.--spoofing-file: The file of the scores file (4-column) containing the scores for the verification system, Spoofing Attack scenario.--output-file: (Optional) Specify the name of the output pdf file.--demandedplot: Specify the option to plotting the score distribution for licit and spoof scenario and threshold line and probability of success line.Again, the most usual way to compute the resulting tables could be:Evaluate experiments on VERA:$ bin/icb2015_evaluate.py -vvvCite our paperIf you use the results in any of your contributions, please cite the following paper:@inproceedings{Tome_ICB2015-SpoofingPalmvein,
     author = {Tome, Pedro and Marcel, S{\'{e}}bastien},
   keywords = {Biometrics, Palm vein, Spoofing Attacks},
      month = may,
      title = {On the Vulnerability of Palm Vein Recognition to Spoofing Attacks},
  booktitle = {The 8th IAPR International Conference on Biometrics (ICB)},
       year = {2015},
   location = {Pucket, Thailand},
        url = {http://publications.idiap.ch/index.php/publications/show/3096}
}