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Software package to reproduce Evaluation Methodologies for Biometric Presentation Attack Detection chapter of Handbook of Biometric Anti-Spoofing: Presentation Attack Detection 2nd Edition

Project description

This package is part of the signal-processing and machine learning toolbox Bob. It is a software package to reproduce “Evaluation Methodologies for Biometric Presentation Attack Detection” chapter of “Handbook of Biometric Anti- Spoofing: Presentation Attack Detection 2nd Edition”:

@INCOLLECTION{Chingovska_SPRINGER_2019,
         author = {Chingovska, Ivana and Mohammadi, Amir and Anjos, Andr{\'{e}} and Marcel, S{\'{e}}bastien},
         editor = {Marcel, S{\'{e}}bastien and Nixon, Mark and Fierrez, Julian and Evans, Nicholas},
          title = {Evaluation Methodologies for Biometric Presentation Attack Detection},
      booktitle = {Handbook of Biometric Anti-Spoofing},
        edition = {2nd},
        chapter = {20},
           year = {2019},
      publisher = {Springer International Publishing},
           isbn = {978-3-319-92627-8},
            url = {https://www.springer.com/us/book/9783319926261},
            doi = {10.1007/978-3-319-92627-8},
       crossref = {Chingovska_Idiap-Internal-RR-30-2018},
            pdf = {https://publidiap.idiap.ch/downloads//papers/2018/Chingovska_SPRINGER_2019.pdf}
}

Reproduction

The installation instructions are based on conda and works on Linux and MacOS systems only. Install conda before continuing.

Once you have installed conda, create a conda environment with the following command and activate it:

$ conda create --name bob.hobpad2.chapter20 --override-channels \
  -c https://www.idiap.ch/software/bob/conda -c defaults \
  bob.hobpad2.chapter20
$ conda activate bob.hobpad2.chapter20

This will install all the required software to reproduce this book chapter. Once installed, follow the commands below to generate the plots:

$ # To generate Figure 4:
$ bob measure gen generic_scores
$ bob measure hist generic_scores/scores-dev -o fig4.a.pdf
$ bob measure det generic_scores/scores-dev -o fig4.b.pdf --lines-at ' ' --no-disp-legend --titles ' '
$ bob measure epc generic_scores/scores-{dev,eval} -o fig4.c.pdf --titles ' ' --no-disp-legend -xl '$\beta$'
$ # To generate Figure 5:
$ bob vulnerability gen vuln_scores
$ bob vulnerability hist vuln_scores/{licit,spoof}/scores-dev -o fig5.a.pdf --no-iapmr-line
$ bob vulnerability hist vuln_scores/{licit,spoof}/scores-dev -o fig5.b.pdf --no-real-data
$ bob vulnerability det vuln_scores/{licit,spoof}/scores-dev -o fig5.c.pdf --fnmr 0.0214 --no-real-data --title ' '
$ bob vulnerability fmr_iapmr vuln_scores/{licit,spoof}/scores-{dev,eval} -o fig5.d.pdf --no-disp-legend --title ' '
$ bob vulnerability epc vuln_scores/{licit,spoof}/scores-{dev,eval} -o fig5.e.pdf --title ' '
$ bob vulnerability epsc vuln_scores/{licit,spoof}/scores-{dev,eval} -o fig5.f.pdf -nI --titles 'EPSC with $\beta = 0.50$' --no-disp-legend

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