The Fingervein Recognition Library

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

$ bin/fingerveinverify.py --help

About

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

Databases

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

• UTFVP - University of Twente Finger Vein Database [http://website]
• VERA Finger vein Database [http://www.idiap.ch/scientific-research/resources]

Algorithms

Together with that, a broad variety of traditional and state-of-the-art finger vein recognition algorithms such as:

• Maximum Curvature [MNM+05]
• Repeated Line Tracking [MNM+04]
• Wide Line Detector [HDLTL+10]

is provided. Furthermore, tools to evaluate the results can easily be used to create scientific plots, and interfaces to run experiments using parallel processes or an SGE grid are provided.

Extensions

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

References

Installation

To download the FingerVeinRecLib, please go to http://pypi.python.org/pypi/fingerveinreclib, click on the download button and extract the .zip file to a folder of your choice.

The FingerVeinRecLib is a satellite package of the free signal processing and machine learning library Bob, and some of its algorithms rely on the `CSU Face Recognition Resources`_. These two dependencies have to be downloaded manually, as explained in the following.

Bob

You will need a copy of Bob in version 1.2.0 or newer to run the algorithms. Please download Bob from its webpage. After downloading, you should go to the console and write:

$ python bootstrap.py
$ bin/buildout

This will download all required packages and install them locally. If you don't want all the database packages to be downloaded, please remove the xbob.db.[database] lines from the eggs section of the file buildout.cfg in the main directory before calling the three commands above.

Test your installation

To verify that your installation worked as expected, you might want to run our test utilities:

$ bin/nosetests

Usually, all tests should pass, if you use the latest packages of Bob. With other versions of Bob, you might find some failing tests, or some errors might occur.

Cite our paper

If you use the FingerVeinRecLib 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}

}