Abstract

Most finger vein recognition systems use palmar finger images. There is some work on the dorsal view, but the remaining views have not been sufficiently investigated yet. All major public available finger-vein databases contain only images from the palmar view and only one smaller database has images from the dorsal view. We aim to fill this gap and evaluate the performance using other perspectives than dorsal and palmar. Therefore, we established a new finger vein data set that consists of videos showing the vein structure all around the finger. We carried out several experiments utilizing common finger-vein recognition algorithms to quantify the recognition performance of each single projection. We further analyzed if a fusion of different views can improve the recognition performance of the system.

Reference

[Prommegger18a      ] Multi-Perspective Finger-Vein Biometrics Bernhard Prommegger, Christof Kauba, Andreas Uhl In 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1-9, Los Angeles, California, USA, October 22 - October 25

Data Set

PLUSVein Finger Rotation Data Set

The PLUSVein Finger Rotation Data Set (PLUSVein-FR) is a partly publically available finger vein data set. It contains finger images captured all around the finger from 63 different subjects, 4 fingers (index and middle finger from both hands) per subject, which sums up to a total of 252 unique fingers. Further information regarding the data set can be found by the following link:

PLUSVein-FR Data Set

Evaluation Framework Information

The experimental evaluations have been conducted using the open source vein recognition framework (PLUS OpenVein Finger- and Hand-Vein Toolkit) provided by the University of Salzburg. This is a feature extraction and matching/evaluation framework for finger- and hand-vein recognition implemented in MATLAB. It was tested on MATLAB 2016 and should work with all version of MATLAB newer or equal to 2016. This software is under the Simplified BSD license.

A more detailed description of the framework as well as its sources can be found here:

The framework contains all the feature extraction, comparison as well as evaluation methods used for the experiments in the paper.