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Inverse Biometrics: Reconstructing Grayscale Finger Vein Images from Binary Features

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.

Inverse Biometrics: Reconstructing Grayscale Finger Vein Images from Binary Features

Finger vein images are reconstructed from their binary feature templates using a newly proposed deep-learning based alogrithm that has been investigated in the scope of the IAPR/IEEE International Joint Conference on Biometrics (IJCB2020) paper "Inverse Biometrics: Reconstructing Grayscale Finger Vein Images from Binary Features". The corresponding information to necessary setting-files and recognition result files can be downloaded from this site to comply with the principles of reproducible research.

Abstract

In this work, we investigate the possibility of generating a grayscale image of the finger vein from its binary tem- plate. This exercise would allow us to determine the invert- ibility of finger vein templates, and this has implications in biometric security and privacy. While such an analy- sis has been undertaken in the context of face, fingerprint and iris templates, this is the first work involving the finger vein biometric trait. The transformation from binary fea- tures to a grayscale image is accomplished using a Pix2Pix Convolutional Neural Network (CNN). The reversibility of 6 different types of binary features is evaluated using this CNN. Further, a number of experiments are conducted us- ing 7 distinct finger vein datasets. Results indicate that (a) it is possible to reconstruct finger vein images from their bi- nary templates; (b) the reconstructed images can be used for biometric recognition purposes; (c) the CNN trained on one dataset can be successfully used for reconstructing im- ages in a different dataset (cross-dataset reconstruction); and (d) the images reconstructed from one set of features can be successfully used to extract a different set of features for biometric recognition (cross-feature-set generalization).

Reference

[Kauba20a  ] Inverse Biometrics: Reconstructing Grayscale Finger Vein Images from Binary Features Christof Kauba, Simon Kirchgasser, Vahid Mirjalili, Arun Ross, Andreas Uhl In Proceedings of the IAPR/IEEE International Joint Conference on Biometrics (IJCB2020), pp. 1-8, Houston, Texas, USA, September 28 - October 1, accepted

Data Sets

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)

Finger Vein USM (FV-USM) Database

The Finger Vein USM (FV-USM) Database is a publically available finger vein data set. The images in the database were collected from 123 volunteers comprising of 83 males and 40 females, who were staff and students of Universiti Sains Malaysia. Further information regarding the data set can be found by the following link:

FV-USM Download Page (external link)

The Chonbuk University Finger Vein Database

The Chonbuk University in South Korea used their prototype scanner (Chonbuk Proto) to establish the MMCBNU_6000 finger vein database. This dataset is currently not publicly available, but further information is provided in the following paper "An available database for the research of finger vein recognition":

IEEE paper 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.

Result Files and Settings

Two resources can be downloaded to obtain the mandatory information necessary for performing the experiments:

  • All settings files utilised by the PLUS OpenVein-Toolkit. For the baseline and reconstruction experiments the settings files are the same.
  • The score and result files obtained during our intra- and inter-dataset evaluation (as presented in the paper). The corresponding baseline results can be re-generated by the use of the datasets, the provided settings files and the OpenVein-Toolkit.

For both resources a separate directory can be found in the downloadable .tar.gz file. One directory contains the setting files while the other one contains the score and result files for both evaluation methods (inter- and intra dataset evaluation). Both directories includes several sub-directories for the utilised datasets and finger vein feature extraction methods.

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

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