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On the Extent of Longitudinal Finger Rotation in Publicly Available Finger Vein Data Sets

This is "The Multimedia Signal Processing and Security Lab", short WaveLab, website. We are a research group at the Computer Sciences Department of the University of Salzburg headed by Andreas Uhl. The short name "WaveLab" already indicates that wavelets are among our favorite tools - we have 15 years of experience in this area. Our research is focused on Multimedia Security including Watermarking, Image and Video Compression, Medical Image Classification, and Biometrics.

Abstract

Recently, concerns regarding potential biases in the underlying algorithms of many automated systems (including biometrics) have been raised. In this context, a biased algorithm produces statistically different outcomes for different groups of individuals based on certain (often protected by anti-discrimination legislation) attributes such as sex and age. While several preliminary studies investigating this matter for facial recognition algorithms do exist, said topic has not yet been addressed for vascular biometric characteristics. Accordingly, in this paper, several popular types of recognition algorithms are benchmarked to ascertain the matter for fingervein recognition. The experimental evaluation suggests lack of bias for the tested algorithms, although future works with larger datasets are needed to validate and confirm those preliminary results.

Reference

[Drozdowski20a   ] Demographic Bias: A Challenge for Fingervein Recognition Systems? Pawel Drozdowski, Bernhard Prommegger, Georg Wimmer, Rudolf Schraml, Christian Rathgeb, Andreas Uhl, Christoph Busch In Proceedings of the 2020 European Signal Processing Conference (EUSIPCO'20), pp. 1-6, Amsterdam, The Nederlands, 2020, accepted

Data Sets

MMCBNU_6000

The MMCBNU_6000 Database is a publicly available finger vein data set. It contains six fingers (ring, middle and index finger from both hands) from 100 volunteers. Further information regarding the data set can be found by the following link:

MMCBNU_6000 Download Page (external link)

PLUSVein-FV3 Finger Vein Data Set

The PLUSVein-FV3 Finger Vein Data Set (PLUSVein-FV3) is a publically available fingr vein data set. It contains palmar and dorsal images of 360 fingers from 60 different subjects (ring, middle and index finger from both hands) captured in one session with five samples per finger using two different variants of the same sensor: One utilizing NIR laser modules for illumination, the other one using NIR LEDs. Further information regarding the data set can be found by the following link:

PLUSVein-FV3 Data Set

University of Twente Finger Vascular Pattern Database

The University of Twente Finger Vascular Pattern (UTFVP) Database is a publically available finger vein data set. It contains six fingers (ring, middle and index finger from both hands) from 60 volunteers acquired in two sessions. Further information regarding the data set can be found by the following link:

UTFVP Download Page (external link)

Idiap Research Institute VERA Fingervein Database

The Idiap Research Institute VERA Fingervein Database (VERA) is a publicly available finger vein data set. It consists of 440 images from 110 clients. The dataset also contains presentation (a.k.a. spoofing) attacks to the same 440 images that can be used to study vulnerability of biometric systems or presentation attack detection schemes. Further information regarding the data set can be found by the following link:

VERA Download Page (external link)

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:

PLUS OpenVein Finger- and Hand-Vein Toolkit

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

Results

Coming soon ...

Coming soon ...