Commit bc0745df authored by André Anjos's avatar André Anjos 💬

[doc] Add more baseline results

parent 26689f96
Pipeline #2890 passed with stage
in 3 minutes and 50 seconds
......@@ -133,7 +133,6 @@ performance:
HTER: 26.421%
Maximum Curvature with Miura Matching
=====================================
......@@ -141,7 +140,7 @@ You can find the description of this method on the paper from Miura *et al.*
[MNM05]_.
To run the baseline on the `VERA fingervein`_ database, using the ``NOM``
protocol (called ``Full`` in [TVM14]_), do the following:
protocol like above, do the following:
.. code-block:: sh
......@@ -149,43 +148,49 @@ protocol (called ``Full`` in [TVM14]_), do the following:
$ ./bin/verify.py --database=vera --protocol=NOM --preprocessor=nopp --extractor=maximumcurvature --algorithm=match-mc --sub-directory="mc" --verbose --verbose
.. tip::
This command line selects and runs the following implementations for the
toolchain, with comparison to the previous baseline:
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``.
* Feature extractor: Maximum Curvature, as explained in [MNM05]_
In a 4-core machine and using 4 parallel tasks, it takes as around 1 hour and
40 minutes to process this baseline with the current code implementation.
Results we obtained:
This command line selects and runs the following implementations for the
toolchain:
.. code-block:: sh
* Database: Use the base Bob API for the VERA database implementation,
protocol variant ``NOM`` which corresponds to the ``Full`` evaluation
protocol described in [TVM14]_
* Preprocessor: Simple finger cropping, with no extra post-processing, as
defined in [LLP09]_
* Feature extractor: Repeated line tracking, as explained in [MNM04]_
* Matching algorithm: "Miura" matching, as explained on the same paper
* Subdirectory: This is the subdirectory in which the scores and intermediate
results of this baseline will be stored.
$ ./bin/bob_eval_threshold.py --scores <path-to>/vera/rlt/NOM/nonorm/scores-dev --criterium=eer
('Threshold:', 0.078274325)
FAR : 3.182% (1533/48180)
FRR : 3.182% (7/220)
HTER: 3.182%
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 as around 2 hours to process this baseline with the
current code implementation.
Wide Line Detector with Miura Matching
======================================
You can find the description of this method on the paper from Huang *et al.*
[HDLTL10]_.
To run the baseline on the `VERA fingervein`_ database, using the ``NOM``
protocol like above, do the following:
To complete the evaluation, run the commands bellow, that will output the equal
error rate (EER) and plot the detector error trade-off (DET) curve with the
performance:
.. code-block:: sh
$ ./bin/verify.py --database=vera --protocol=NOM --preprocessor=nopp --extractor=widelinedetector --algorithm=match-wld --sub-directory="wld" --verbose --verbose
This command line selects and runs the following implementations for the
toolchain, with comparison to the previous baseline:
* Feature extractor: Wide Line Detector, as explained in [HDLTL10]_
Results we obtained:
.. code-block:: sh
$ ./bin/bob_eval_threshold.py --scores <path-to>/vera/rlt/NOM/nonorm/scores-dev --criterium=eer
('Threshold:', 0.320748535)
FAR : 26.478% (12757/48180)
FRR : 26.364% (58/220)
HTER: 26.421%
......
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