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Multi-Perspective Enrolment in Finger Vein Recognition

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 recognition of subjects based on their venous pattern within the fingers. It has been shown that its recognition accuracy heavily depends on a good alignment of the acquired samples. There are several approaches that try to reduce the impact of finger misplacement. However, none of these approaches is able to prevent all possible types of finger misplacements. As finger vein scanners are evolving towards contact-less acquisition, alignment problems, especially due to longitudinal finger rotation, are becoming even more important. Along with rotation detection and correction, capturing the vein pattern from multiple perspectives, as e.g. in multiple-perspective enrolment (MPE), is a way to tackle the problem of longitudinal finger rotation. Involving multiple cameras increases cost and complexity of the capturing devices, and therefore their number should be kept to a minimum. Perspective multiplication for MPE (PM-MPE) successfully reduces the number of cameras needed during enrolment while keeping the recognition rates at a high level. So far, (PM-)MPE has only been applied using Maximum curvature features (MC). This work analyses further approaches to improve the their recognition rates and investigates the applicability of (PM-)MPE to recognition schemes using features other than MC.


[Prommegger20a    ] Advanced Multi-Perspective Enrolment in Finger Vein Recognition Bernhard Prommegger, Andreas Uhl In Proceedings of the 8th International Workshop on Biometrics and Forensics (IWBF'20), pp. 1-6, Porto, Portugal, April 29 - April 30

Data Sets

PLUSVein Finger Rotation Data Set

The PLUSVein Finger Rotation Data Set (PLUSVein-FR) is a partly publically available finger vein data set. It contains finger images captured all around the finger from 63 different subjects, 4 fingers (index and middle finger from both hands) per subject, which sums up to a total of 252 unique fingers. Further information regarding the data set can be found by the following link:

PLUSVein-FR Data Set

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 performance results are provided as .tar file that contains one result file per KPI (EER, FMR100, FMR1000 and ZeroFMR). The naming convention of the result files is [RecognitionScheme]/PerformanceResults_[Method]_[RecognitionScheme]_[KPI].txt.

These result can be downloaded below:

Result Files Download

The download files are available upon request.

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