Executing Baseline Algorithms

In this section we introduce the baselines available in this pakcage. To execute one of then in the databases available just run the following command:

$ bob bio pipeline simple [DATABASE_NAME] [BASELINE]

Note

Both, [DATABASE_NAME] and [BASELINE] can be either python resources or python files.

Please, refer to bob.bio.base for more information.

Repeated Line-Tracking with Miura Matching

Detailed description at Repeated Line Tracking and Miura Matching.

To run the baseline on the VERA fingervein database, using the Nom protocol (deprecated), do the following:

$ bob bio pipeline simple verafinger rlt -vv -c

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 by adding the options -l local-parallel.

$ bob bio pipeline simple verafinger rlt -vv -c -l local-parallel

To run on the Idiap SGE grid use:

$ bob bio pipeline simple rlt -vv -c -l sge

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

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:

$ bob bio metrics <path-to>/verafinger/rlt/Nom/nonorm/scores-dev --no-evaluation
[Min. criterion: EER ] Threshold on Development set `scores-dev`: 0.31835292
======  ========================
None    Development scores-dev
======  ========================
FtA     0.0%
FMR     23.6% (11388/48180)
FNMR    23.6% (52/220)
FAR     23.6%
FRR     23.6%
HTER    23.6%
======  ========================

Maximum Curvature with Miura Matching

Detailed description at Maximum Curvature and Miura Matching.

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

$ bob bio pipeline simple verafinger mc -vv -c

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

In a 4-core machine and using 4 parallel tasks, it takes around 1 hour and 40 minutes to process this baseline with the current code implementation. Results we obtained:

$ bob bio metrics <path-to>/verafinger/mc/Nom/nonorm/scores-dev --no-evaluation
[Min. criterion: EER ] Threshold on Development set `scores-dev`: 7.372830e-02
======  ========================
None    Development scores-dev
======  ========================
FtA     0.0%
FMR     4.4% (2116/48180)
FNMR    4.5% (10/220)
FAR     4.4%
FRR     4.5%
HTER    4.5%
======  ========================

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:

$ bob bio pipeline simple verafinger wld -vv -c

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

In a 4-core machine and using 4 parallel tasks, it takes only around 5 minutes minutes to process this baseline with the current code implementation.Results we obtained:

$ bob bio metrics <path-to>/verafinger/wld/Nom/nonorm/scores-dev --no-evaluation
[Min. criterion: EER ] Threshold on Development set `scores-dev`: 2.402707e-01
======  ========================
None    Development scores-dev
======  ========================
FtA     0.0%
FMR     9.8% (4726/48180)
FNMR    10.0% (22/220)
FAR     9.8%
FRR     10.0%
HTER    9.9%

Results for other Baselines

This package may generate results for other combinations of protocols and databases. Here is a summary table for some variants (results expressed correspond to the the equal-error rate on the development set, in percentage):

Toolchain

Vera Finger

UTFVP

Feature Extractor

Full

B

Nom

1vsall

nom

Repeated Line Tracking

14.6

13.4

23.6

3.4

1.4

Wide Line Detector

5.8

5.6

9.9

2.8

1.9

Maximum Curvature

2.5

1.4

4.5

0.9

0.4

In a machine with 48 cores, running these baselines took the following time (hh:mm):

Toolchain

Vera Finger

UTFVP

Feature Extractor

Full

B

Nom

1vsall

nom

Repeated Line Tracking

01:16

00:23

00:23

12:44

00:35

Wide Line Detector

00:07

00:01

00:01

02:25

00:05

Maximum Curvature

03:28

00:54

00:59

58:34

01:48

Modifying Baseline Experiments

It is fairly easy to modify baseline experiments available in this package. To do so, you must copy the configuration files for the given baseline you want to modify, edit them to make the desired changes and run the experiment again.

For example, suppose you’d like to change the protocol on the Vera Fingervein database and use the protocol full instead of the default protocol nom. First, you identify where the configuration file sits:

$ resources.py -tc -p bob.bio.vein
- bob.bio.vein X.Y.Z @ /path/to/bob.bio.vein:
  + mc         --> bob.bio.vein.configurations.maximum_curvature
  + parallel   --> bob.bio.vein.configurations.parallel
  + rlt        --> bob.bio.vein.configurations.repeated_line_tracking
  + utfvp      --> bob.bio.vein.configurations.utfvp
  + verafinger --> bob.bio.vein.configurations.verafinger
  + wld        --> bob.bio.vein.configurations.wide_line_detector

The listing above tells the verafinger configuration file sits on the file /path/to/bob.bio.vein/bob/bio/vein/configurations/verafinger.py. In order to modify it, make a local copy. For example:

$ cp /path/to/bob.bio.vein/bob/bio/vein/configurations/verafinger.py verafinger_full.py
$ # edit verafinger_full.py, change the value of "protocol" to "full"

Also, don’t forget to change all relative module imports (such as from ..database.verafinger import Database) to absolute imports (e.g. from bob.bio.vein.database.verafinger import Database). This will make the configuration file work irrespectively of its location w.r.t. bob.bio.vein. The final version of the modified file could look like this:

from bob.bio.vein.database.verafinger import Database

database = Database(original_directory='/where/you/have/the/raw/files',
  original_extension='.png', #don't change this
  )

protocol = 'full'

Now, re-run the experiment using your modified database descriptor:

$ bob bio pipeline simple ./verafinger_full.py wld -vv -c

Notice we replace the use of the registered configuration file named verafinger by the local file verafinger_full.py. This makes the program verify.py take that into consideration instead of the original file.

Other Resources

This package contains other resources that can be used to evaluate different bits of the vein processing toolchain.

Region of Interest Goodness of Fit

Automatic region of interest (RoI) finding and cropping can be evaluated using a couple of scripts available in this package. The program bob_bio_vein_compare_rois.py compares two sets of preprocessed images and masks, generated by different preprocessors (see bob.bio.base.preprocessor.Preprocessor) and calculates a few metrics to help you determine how both techniques compare. Normally, the program is used to compare the result of automatic RoI to manually annoted regions on the same images. To use it, just point it to the outputs of two experiments representing the manually annotated regions and automatically extracted ones. E.g.:

$ bob_bio_vein_compare_rois.py ~/verafinger/mc_annot/preprocessed ~/verafinger/mc/preprocessed
Jaccard index: 9.60e-01 +- 5.98e-02
Intersection ratio (m1): 9.79e-01 +- 5.81e-02
Intersection ratio of complement (m2): 1.96e-02 +- 1.53e-02

Values printed by the script correspond to the Jaccard index (bob.bio.vein.preprocessor.utils.jaccard_index()), as well as the intersection ratio between the manual and automatically generated masks (bob.bio.vein.preprocessor.utils.intersect_ratio()) and the ratio to the complement of the intersection with respect to the automatically generated mask (bob.bio.vein.preprocessor.utils.intersect_ratio_of_complement()). You can use the option -n 5 to print the 5 worst cases according to each of the metrics.

Pipeline Display

You can use the program bob_bio_vein_view_sample.py to display the images after full processing using:

$ bob_bio_vein_view_sample.py --save=output-dir verafinger /path/to/processed/directory 030-M/030_L_1
$ # open output-dir

And you should be able to view images like these (example taken from the Vera fingervein database, using the automatic annotator and Maximum Curvature feature extractor):

_images/preprocessed.png

Fig. 1 Example RoI overlayed on finger vein image of the Vera fingervein database, as produced by the script bob_bio_vein_view_sample.py.

_images/binarized.png

Fig. 2 Example of fingervein image from the Vera fingervein database, binarized by using Maximum Curvature, after pre-processing.