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Finger Vein Recognition with Classical Fingerprint Minutiae Comparison Techniques

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

Security and privacy is of great interest in biometric systems which can be offered by Match-on-Card (MoC) technology, successfully applied in several areas of biometrics. In finger vein recognition such a system is not available yet. Utilizing minutiae points from vein images in combination with classical minutiae-based fingerprint comparison software offers a great opportunity to integrate vein recognition on MoC systems. In this work a publicly available and two commercial software packages are used to evaluate the recognition performance of vein minutiae on three publicly available databases. The results indicate that minutia-based comparison technology from fingerprint recognition can be applied to finger vein recognition and are able to compete with classical correlation-based methods utilized in this field.

Software Information

Software written in C++ (Cxx11). Needs the opencv library (Tested with 3.4.2). Is a CMake project (requires CMake version 3.4).

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The download files are available upon request.

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