home  |   research   |  members   |  projects  |  publications   |  conferences of interest   |  downloads   |  contact  |  intern
 
 

Combined Fully Contact-Less Finger and Hand Vein Acquisition Device - Evaluation Framework and Scores Files

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.

Evaluation Framework Information

The experimental evaluations have been conducted using the open source vein recognition framework (PLUS OpenVein 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.

The framework contains all the feature extraction and matching as well as evaluation methods used for the experiments in the paper:

Christof Kauba, Bernhard Prommegger, and Andreas Uhl "Combined Fully Contact-Less Finger and Hand Vein Acqusition Device with a Corresponding Data Set" In MDPI Sensors, Volume XXX, 18 pages, October 2019.


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

PLUS OpenVein Toolkit

Data Set

PLUSVein-Contactless Finger and Hand Vein Data Set

The finger and hand vein data set used during the evaluations is publicly available for research and non-commercial purposes and can be requested here:

PLUSVein-Contactless Database

Further informations regarding the data set can be found by following the above link.

Scores Files

General Structure of the Files

The scores files are provided as MATLAB .mat files. Each .mat file contains a struct, containing two vectors:

  • positives: This vector contains the scores obtained from the genuine matches.
  • negatives: This vector contains the scores obtained from the impostor matches.

The scores are all similarity scores, i.e. higher scores indicate higher similarity. Thus the genuine scores should be ideally higher than the impostor ones. The scores obtained for the 3 binary features using the Miura matcher are in the range of [0 - 0.5] while the scores obtained for SIFT are in the range of [0 - 1].

File Naming Conventions and Directory Structure

According to the experiments, there are three different sets of score files: one for the light transmission illuminated finger vein images, one for the reflected light 850 nm illuminated hand vein images and one for the reflected light 950 nm illuminated hand vein images.

The directory structure is as follows:

  • Scores: contains all the scores files
    • FingerVein_TI: Contains the scores files for the laser light transmission illuminated finger vein subset
    • HandVein_850: Contains the scores files for the 850 nm LED reflected light illuminated hand vein subset
    • HandVein_950: Contains the scores files for the 950 nm LED reflected light illuminated hand vein subset
  • Settings: contains the settings files to be used with the OpenVein Toolkit evaluation framework to arrive at these scores based on the PLUSVein-Contactless database
    • FingerVein_TI: The settings files for the finger vein subst
    • HandVein_850nm: The settings files for the 850 nm hand vein subst
    • HandVein_950nm: The settings files for the 950 nm hand vein subst

Each of the subdirectories contains one scores file or one settings file per feature type, respectively:

  • Scores_GF.mat: Scores obtained for the evaluation of the Gabor Filter based features.
  • Scores_MC.mat: Scores obtained for the evaluation of the Maximum Curvature based features.
  • Scores_PC.mat: Scores obtained for the evaluation of the Principal Curvature based features.
  • Scores_SIFT.mat: Scores obtained for the evaluation of the SIFT based features.
These score and settings files can be downloaded below:

Scores Files and Settings Download

The scores and settings are available upon request.

Please fill out this form to request a download link for the scores and settings files:

Name:
Affiliation:
Email address: