Implementation of the local and global unlinkability metrics for biometric template protection systems evaluation proposed in [TIFS18].

This work is licensed under license agreement provided by Hochschule Darmstadt (h_da-License).

• seaborn
• numpy
• pylab
• matplotlib
• argparse

1. Run evaluateUnlinkability.py

   usage: evaluateUnlinkability.py [-h] [--omega [OMEGA]] [--nBins [NBINS]]
   								[--figureTitle [FIGURETITLE]]
   								[--legendLocation [LEGENDLOCATION]]
   								matedScoresFile nonMatedScoresFile figureFile
   
   Evaluate unlinkability for two given sets of mated and non-mated linkage
   scores.
   
   positional arguments:
     matedScoresFile       filename for the mated scores
     nonMatedScoresFile    filename for the non-mated scores
     figureFile            filename for the output figure
   
   optional arguments:
     -h, --help            show this help message and exit
     --omega [OMEGA]       omega value for the computations, if none provided,
   						omega = 1
     --nBins [NBINS]       number of bins for the computations, if none provided,
   						nBins = 100
     --figureTitle [FIGURETITLE]
   						title for the output figure
     --legendLocation [LEGENDLOCATION]
   						legend location

2. Input: at least 3 score files (mated and non-mated score examples provided), and optionally other parameters of the computation and the formatting of the figure obtained as output.

   The score files are loaded with the built-in function numpy.fromfile(). An example in hdf5 format has been provided, but other formats, such as a txt file with all scores separated by blank spaces or one score per row, can be also used.

3. Output: figure with score distributions, point-wise and global unlinkability metric results.

More details in:

• [TIFS18] M. Gomez-Barrero, J. Galbally, C. Rathgeb, C. Busch, "General Framework to Evaluate Unlinkability in Biometric Template Protection Systems", in IEEE Trans. on Informations Forensics and Security, vol. 3, no. 6, pp. 1406-1420, June 2018.

Please remember to reference article [TIFS18] on any work made public, whatever the form, based directly or indirectly on these metrics.