baselines.rst

Executing Baseline Algorithms

The first thing you might want to do is to execute one of the vein recognition algorithms that are implemented in bob.bio.vein.

Running Baseline Experiments

To run the baseline experiments, you can use the ./bin/verify.py script by just going to the console and typing:

$ ./bin/verify.py

This script is explained in more detail in :ref:`bob.bio.base.experiments`. The ./bin/verify.py --help option shows you, which other options you can set.

Usually it is a good idea to have at least verbose level 2 (i.e., calling ./bin/verify.py --verbose --verbose, or the short version ./bin/verify.py -vv).

Note

Running in Parallel

To run the experiments in parallel, you can define an SGE grid or local host (multi-processing) configurations as explained in :ref:`running_in_parallel`.

In short, to run in the Idiap SGE grid, you can simply add the --grid command line option, without parameters. To run experiments in parallel on the local machine, simply add a --parallel <N> option, where <N> specifies the number of parallel jobs you want to execute.

In the remainder of this section we introduce baseline experiments you can readily run with this tool without further configuration. Baselines examplified in this guide were published in [TVM14]_.

Database setups and baselines are encoded using :ref:`bob.bio.base.configuration-files`, all stored inside the package root, in the directory bob/bio/vein/configurations. Documentation for each resource is available on the section :ref:`bob.bio.vein.resources`.

Warning

You cannot run experiments just by executing the command line instructions described in this guide. You need first to procure yourself the raw data files that correspond to each database used here in order to correctly run experiments with those data. Biometric data is considered private date and, under EU regulations, cannot be distributed without a consent or license. You may consult our :ref:`bob.bio.vein.resources.databases` resources section for checking currently supported databases and accessing download links for the raw data files.

Once the raw data files have been downloaded, particular attention should be given to the directory locations of those. Unpack the databases carefully and annotate the root directory where they have been unpacked.

Then, carefully read the Databases section of :ref:`bob.bio.base.installation` on how to correctly setup the ~/.bob_bio_databases.txt file.

Use the following keywords on the left side of the assignment (see :ref:`bob.bio.vein.resources.databases`):

[YOUR_VERAFINGER_DIRECTORY] = /complete/path/to/verafinger
[YOUR_UTFVP_DIRECTORY] = /complete/path/to/utfvp
[YOUR_BIOWAVE_TEST_DIRECTORY] = /complete/path/to/biowave_test

Notice it is rather important to use the strings as described above, otherwise bob.bio.base will not be able to correctly load your images.

Once this step is done, you can proceed with the instructions below.

Repeated Line-Tracking with Miura Matching

Detailed description at :ref:`bob.bio.vein.resources.recognition.rlt`.

To run the baseline on the `VERA fingervein`_ database, using the Nom protocol, do the following:

$ ./bin/verify.py verafinger rlt -vv

Tip

If you have more processing cores on your local machine and don't want to submit your job for SGE execution, you can run it in parallel (using 4 parallel tasks) by adding the options --parallel=4 --nice=10.

This command line selects and runs the following implementations for the toolchain:

• :ref:`bob.bio.vein.resources.database.verafinger`
• :ref:`bob.bio.vein.resources.recognition.rlt`

As the tool runs, you'll see printouts that show how it advances through preprocessing, feature extraction and matching. In a 4-core machine and using 4 parallel tasks, it takes around 4 hours to process this baseline with the current code implementation.

To complete the evaluation, run the command bellow, that will output the equal error rate (EER) and plot the detector error trade-off (DET) curve with the performance:

$ ./bin/bob_eval_threshold.py  --scores <path-to>/verafinger/rlt/Nom/nonorm/scores-dev --criterium=eer
('Threshold:', 0.32045327)
FAR : 26.362% (12701/48180)
FRR : 26.364% (58/220)
HTER: 26.363%

Maximum Curvature with Miura Matching

Detailed description at :ref:`bob.bio.vein.resources.recognition.mc`.

To run the baseline on the `VERA fingervein`_ database, using the Nom protocol like above, do the following:

$ ./bin/verify.py verafinger mc -vv

This command line selects and runs the following implementations for the toolchain:

• :ref:`bob.bio.vein.resources.database.verafinger`
• :ref:`bob.bio.vein.resources.recognition.mc`