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 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 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.
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).
[Kauba20a ] Inverse Biometrics: Reconstructing Grayscale Finger Vein Images from Binary Features In Proceedings of the IAPR/IEEE International Joint Conference on Biometrics (IJCB2020), pp. 1-8, Houston, Texas, USA, September 28 - October 1
PLUSVein-FV3 Finger Vein Data Set
The PLUSVein-FV3 Finger Vein Data Set (PLUSVein-FV3) is a publically available finger 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:
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 (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:
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":
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: