Skip to main content

Fingervein recognition based on Bob and the facereclib

Project description

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/ --help


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.


When you are working at Idiap, you might get a version of the FingerVeinRecLib, where all paths are set up such that you can directly start running experiments. Outside Idiap, you need to set up the paths to point to your databases, please check Read Further on how to do that.


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


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

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.


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.


[MNM+05]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.
[MNM+04]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.
[HDLTL+10]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.


To download the FingerVeinRecLib, please go to, 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.


You will need a copy of Bob in version 1.2.0 or newer to run the algorithms. Please download Bob from its webpage.


You will need a copy of FaceRecLib in version 1.2.3 or newer to run the algorithms. Please download FaceRecLib from its webpage.

Remember update the buildout.cfg file with the path of your data and package dependencies.

After downloading, you should go to the console and write:

$ python
$ 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 Bob in version 1.2.1. With other versions of Bob, you might find some failing tests.

Cite our paper

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

       author = {Tome, Pedro and Vanoni, Matthias and Marcel, S{\'{e}}bastien},
     keywords = {Biometrics, Finger vein, Spoofing Attacks},
        month = sep,
        title = {On the Vulnerability of Finger Vein Recognition to Spoofing},
    booktitle = {IEEE International Conference of the Biometrics Special Interest Group (BIOSIG)},
       series = {},
       volume = {},
         year = {2014},
        pages = {},
     location = {Darmstadt, Germay},
          url = {}

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for xbob.fingervein, version 1.0.0
Filename, size File type Python version Upload date Hashes
Filename, size (54.0 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page