[doc] Add max. curvature baseline

doc/baselines.rst

@@ -116,26 +116,77 @@ toolchain:
 
 
 As the tool runs, you'll see printouts that show how it advances through
-preprocessing, feature extraction and matching. 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:
+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.
+
+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/bob_eval_threshold.py  --scores <path-to>/vera/rlt/NOM/nonorm/scores-dev --criterium=eer
-   ('Threshold:', 0.32023322499999995)
-   FAR : 24.318% (46866/192720)
-   FRR : 24.318% (107/440)
-   HTER: 24.318%
-   $ ./bin/evaluate.py --dev-files <path-to>/vera/rlt/NOM/nonorm/scores-dev --det det.pdf -l "vera-nom-mnm04" -rr
-   The Recognition Rate of the development set of 'rlt' is 48.409%
+   ('Threshold:', 0.320748535)
+   FAR : 26.478% (12757/48180)
+   FRR : 26.364% (58/220)
+   HTER: 26.421%
+
+
+
+Maximum Curvature with Miura Matching
+=====================================
+
+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:
 
-To view the DET curve stored in the output file, do the following (on a Linux
-machine):
 
 .. code-block:: sh
 
-   $ xdg-open det.pdf #to view the DET curve
+   $ ./bin/verify.py --database=vera --protocol=NOM --preprocessor=nopp --extractor=maximumcurvature --algorithm=match-mc --sub-directory="mc" --verbose --verbose
+
+
+.. 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:
+
+* 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.
+
+
+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.
+
+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/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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