Abstract

Abstract In finger vein recognition the palmar view of the finger is used almost exclusively, with some exceptions where the dorsal view is utilised. Only little attention has been paid to all other views around the finger's longitudinal axis. We established a multi-perspective finger vein data set comprising of views all around the finger's longitudinal axis, captured using our self-developed rotating multi-perspective finger vein capture device. The performance of the single views is evaluated using common finger vein recognition algorithms. Based on these single view scores several score-level fusion experiments involving different fusion strategies are carried out in order to determine the best performing set of views and feature extraction methods to be fused in terms of recognition accuracy while minimising the number of views involved. Our experimental results show that the recognition performance can be significantly improved over the best performing single view one with as few as two views and two feature extraction methods involved.

Reference

[Prommegger19b   ] Different Views on the Finger — Score-Level Fusion in Multi-Perspective Finger Vein Recognition Bernhard Prommegger, Christof Kauba, Andreas Uhl In Andreas Uhl, Christoph Busch, Sebastien Marcel, Raymond Veldhuis, editors, Handbook of Vascular Biometrics, pp. 261-305, Cham, Switzerland, Springer Nature Switzerland AG, 2019

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.

Result Files and Settings

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