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 Artificial Intelligence and Human Interfaces (AIHI) Department of the University of Salzburg led by Andreas Uhl. Our research is focused on Visual Data Processing and associated security questions. Most of our work is currently concentrated on Biometrics, Media Forensics and Media Security, Medical Image and Video Analysis, and application oriented fundamental research in digital humanities, individualised aquaculture and sustainable wood industry.
Finger vein recognition deals with the identification of a subjects based on its venous pattern within the fingers. The majority of the publicly available finger vein data sets has been acquired with the help of scanner devices that capture a single finger from the palmar side using light transmission. Some of them are equipped with a contact surface or other structures to support in finger placement. However, these means are not able to prevent all possible types of finger misplacements, in particular longitudinal finger rotation can not be averted. It has been shown that this type of finger rotation results in a non-linear deformation of the vein structure, causing severe problems to finger vein recognition systems. So far it is not known if and to which extent this longitudinal finger rotation is present in publicly available finger vein data sets. This paper evaluates the presence of longitudinal finger rotation and its extent in four publicly available finger vein data sets and provides the determined rotation angles to the scientific public. This additional information will increase the value of the evaluated data sets. To verify the correctness of the determined rotation angles, we furthermore demonstrate that employing a simple rotation correction, using those rotation angles, improves the recognition performance.
[Prommegger19c ] On the Extent of Longitudinal Finger Rotation in Publicly Available Finger Vein Data Sets In Proceedings of the 12th IAPR/IEEE International Conference on Biometrics (ICB'19), pp. 1-8, Crete, Greece, June 4 - June 7
The SDUMLA-HMT database is a multimodal biometric database that contains samples for face, gait, iris, fingerprint and finger veins from 106 individuals. The finger vein subset contains six fingers (ring, middle and index finger from both hands) per subject, captured in one session taking six images of each finger. Further information regarding the data set can be found by the following link:
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:
Finger Vein USM Database
Finger Vein USM (FV-USM) Database is a publically available finger vein data set. It contains finger vein images from 123 volunteers, four fingers each (left and right index and middle finger). The data was captured in two different sessions, capturing six samples per finger in each session. Further information regarding the data set can be found by the following 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:
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.
The estimated rotation angles are provided as .tar files that contains one result file per data set. The content of the tab-delimited files are: