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Longitudinal Finger Rotation - Deformation Detection and Correction

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 biometrics is becoming more and more popular. However, longitudinal finger rotation, which can easily occur in practical applications, causes severe problems as the resulting vein structure is deformed in a non-linear way. These problems will become even more important in the future, as finger vein scanners are evolving towards contact-less acquisition. This paper provides a systematic evaluation regarding the influence of longitudinal rotation on the performance of finger vein recognition systems and the degree to which the deformations can be corrected. It presents two novel approaches to correct the longitudinal rotation, one based on the known rotation angle. The second one compensates the rotational deformation by applying a rotation correction in both directions using a pre-defined angle combined with score level fusion and works without any knowledge of the actual rotation angle. During the experiments, the aforementioned approaches and two additional are applied: one correcting the deformations based on an analysis of the geometric shape of the finger and the second one applying a elliptic pattern normalization of the region of interest. The experimental results confirm the negative impact of longitudinal rotation on the recognition performance and prove that its correction noticeably improves the performance again.


[Prommegger19a     ] Longitudinal Finger Rotation - Deformation Detection and Correction Bernhard Prommegger, Christof Kauba, Michael Linortner, Andreas Uhl IEEE Transactions on Biometrics, Behavior, and Identity Science 1:2, pp. 123-138, 2019

Data Set

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.

Result Files and Settings

The result files are provided as ASCII .txt files. Each file contains the results for all recognition schemes grouped by the correction method (fixed angle, elliptic pattern normalization, ...) and performance indicator (EER, FMR100, FMR1000, ZeroFMR). The naming convention is Results_[Correction Method]_[Performance Indicator].txt. The lines corresponds to the different rotation angles from -45° to +45°, the columns to the used recognition schemes.

According to the experiments, we used the same parameter settings for every rotation correction method. The settings files are provided per recognition scheme. The naming convention is Settings_[Recognition Scheme]_TBIOM.ini.


  • [Correction Method]
    • NoCorrection: Baseline Results
    • KnownAngle: Rotation Compensation for Known Rotation Angle
    • GADC: Rotation Compensation Using Geometric Shape Analysis
    • EllipticCorrection: Rotation Compensation Using Elliptic Pattern Normalization
    • FixedAngle: Rotation Compensation Using a Fixed Rotation Angle
    • FixedAngleElliptic: Rotation Compensation Using a Fixed Rotation Angle + Elliptic Pattern Normalization
  • [Performance Indicator]
    • EER: Equal error rate
    • FMR100: The lowest FNMR for FMR ≤ 1%
    • FMR1000: The lowest FNMR for FMR ≤ 0,1%
    • ZeroFMR: The lowest FNMR for FMR = 0%
  • [Recognition Scheme]
    • MC: Maximum Curvature
    • PC: Principal Curvature
    • DTFPM: Deformation-Tolerant Feature-Point Matching
    • WLD: Wide Line Detector
    • GF: Gabor Filter
    • SIFT: Scale-Invariant Feature Transform
    • ASAVE: Finger Vein Recognition With Anatomy Structure Analysis
These result and settings files can be downloaded below:

Result Files and Settings Download

The results and settings files are available upon request.

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

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