README.rst

The Biometrics Vein Recognition Library

Welcome to this Vein Recognition Library based on Bob. This library is designed to perform a fair comparison of vein recognition algorithms. It contains scripts to execute various of vein recognition experiments on a variety of vein image databases, and running the help is as easy as going to the command line and typing:

$ bin/verify.py --help

About

This library is currently developed at the Biometrics group at the Idiap Research Institute. The vein recognition library is designed to run vein recognition experiments in a comparable and reproducible manner.

Databases

To achieve this goal, interfaces to some publicly available vein image databases are contained, and default evaluation protocols are defined, e.g.:

• UTFVP - University of Twente Finger Vein Database [http://www.sas.ewi.utwente.nl/]
• VERA - Finger vein Database [http://www.idiap.ch/scientific-research/resources]
• PUT - The PUT biometric vein (palm and wrist) recognition dataset [http://biometrics.put.poznan.pl/vein-dataset/]

Algorithms

Together with that, implementations of a variety of traditional and state-of-the-art vein recognition algorithms are provided:

• Maximum Curvature [MNM05]
• Repeated Line Tracking [MNM04]
• Wide Line Detector [HDLTL10]

Tools to evaluate the results can easily be used to create scientific plots. We also provide handles to run experiments using parallel processes or an SGE grid.

Extensions

On top of these already pre-coded algorithms, the vein recognition library provides an easy Python interface for implementing new image preprocessors, feature types, vein recognition algorithms or database interfaces, which directly integrate into the vein recognition experiment. Hence, after a short period of coding, researchers can compare new ideas directly with already existing algorithms in a fair manner.

References

[MNM05]

N. Miura, A. Nagasaka, and T. Miyatake. Extraction of Finger-Vein Pattern Using Maximum Curvature Points in Image Profiles. Proceedings on IAPR conference on machine vision applications, 9, pp. 347--350, 2005.

[MNM04]

N. Miura, A. Nagasaka, and T. Miyatake. Feature extraction of finger vein patterns based on repeated line tracking and its application to personal identification. Machine Vision and Applications, Vol. 15, Num. 4, pp. 194--203, 2004.

[HDLTL10]

B. Huang, Y. Dai, R. Li, D. Tang and W. Li. Finger-vein authentication based on wide line detector and pattern normalization. Proceedings of the 20th International Conference on Pattern Recognition (ICPR), 2010.

Installation

The latest version of the vein recognition library can be installed with our Conda-based builds. Once you have installed Bob, just go on and install this package using the same installation mechanism.

Development

In order to develop the latest version of this package, install Bob as indicated above. Once that is done, do this:

$ git clone https://gitlab.idiap.ch/biometrc/bob.bio.vein.git
$ cd bob.bio.vein
$ python bootstrap-buildout.py
$ ./bin/buildout

After those steps, you should have a functional development environment to test the package. The python interpreter and base environment on line 3 above should be pre-installed with all dependencies required for Bob to operate correctly. For example, you may start from our conda-based builds and then use the Python interpreter in there to bootstrap your local development environment.

Running tests

To verify that your installation worked as expected, you might want to run our unit tests with:

$ ./bin/nosetests -sv

Cite our paper

If you use this library in any of your experiments, please cite the following paper:

@inproceedings{Tome_IEEEBIOSIG2014,
    author = {Tome, Pedro and Vanoni, Matthias and Marcel, S{\'{e}}bastien},
    keywords = {Biometrics, Finger vein, Spoofing Attacks},
    projects = {Idiap, BEAT},
    month = sep,
    title = {On the Vulnerability of Finger Vein Recognition to Spoofing},
    booktitle = {IEEE International Conference of the Biometrics Special Interest Group (BIOSIG)},
    volume = {230},
    year = {2014},
    ocation = {Darmstadt, Germay},
    pdf = {http://publications.idiap.ch/downloads/papers/2014/Tome_IEEEBIOSIG2014.pdf}
}