Skip to main content

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”:

         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 = {},
            doi = {10.1007/978-3-319-92627-8},
       crossref = {Chingovska_Idiap-Internal-RR-30-2018},
            pdf = {}


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 -c defaults \
$ 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


For questions or reporting issues to this software package, contact our development mailing list.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for bob.hobpad2.chapter20, version 1.0.2
Filename, size File type Python version Upload date Hashes
Filename, size (35.2 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page