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USIT -- University of Salzburg Iris-Toolkit v2.2.0

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.
This is USIT Version 2 — Version 1 is located here

USIT - University of Salzburg Iris Toolkit v2 is a Windows/Linux software package for iris recognition, made publicly available together with the book chapter:

C. Rathgeb, A. Uhl, P. Wild, and H. Hofbauer. "Design Decisions for an Iris Recognition SDK," in K. Bowyer and M. J. Burge, editors, Handbook of iris recognition, second edition, Advances in Computer Vision and Pattern Recognition, Springer, 2016.

The software package includes algorithms for:

  • Iris Preprocessing
  • Feature Extraction
  • Feature Comparison
USIT is based on easy-to-use command line tools (input and output relies on files). In order to download USIT follow the link on the bottom.

Chapter Abstract:
Open source software development kits are vital to (iris) biometric research in order to achieve comparability and reproducibility of research results. In addition, to further advances in the field of iris biometrics the community needs to be provided with state-of-the-art reference systems which serve as adequate starting point for new research. This chapter provides a summary of relevant design decisions for software modules constituting an iris recognition system. The proposal of general criteria and adequate concepts is complemented by a detailed description of how according design decisions are implemented in the University of Salzburg Iris Toolkit, an open source iris recognition software which contains diverse algorithms for iris segmentation, feature extraction, and comparison. Building upon a file-based processing chain the provided open source software is designed to support rapid prototyping as well as integration in existing frameworks achieving enhanced usability and extensibility. In order to underline the competitiveness of the presented iris recognition software, experimental evaluations of segmentation and feature extraction algorithms are carried out on a publicly available iris database and compared to a commercial product.

Readme

License

H. Hofbauer, C. Rathgeb, A. Uhl, and P. Wild, University of Salzburg, AUSTRIA, 2016

Copyright (c) 2016, University of Salzburg All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

If this software is used to prepare for an article please include the following reference:

Text

C. Rathgeb, A. Uhl, P. Wild, and H. Hofbauer. “Design Decisions for an Iris Recognition SDK,” in K. Bowyer and M. J. Burge, editors, Handbook of iris recognition, second edition, Advances in Computer Vision and Pattern Recognition, Springer, 2016.

Bibtex

@incollection{USIT2,
    author     = {Christian Rathgeb and Andreas Uhl and Peter Wild and Heinz Hofbauer},
    title      = {Design Decisions for an Iris Recognition SDK},
    booktitle  = {Handbook of Iris Recognition},
    editor     = {Kevin Bowyer and Mark J. Burge},
    publisher  = {Springer},
    year       = {2016},
    series     = {Advances in Computer Vision and Pattern Recognition},
    edition    = {second edition},
}

Requirements

These programs require the following libraries:

Algorithm description

Segmentation

  • caht … Contrast-adjusted Hough Transform
  • wahet .. Weighted Adaptive Hough and Ellipsopolar Transform
  • ifpp … Iterative Fourier-series Push Pull
  • manuseg … Uses points from a manual segmentation to extract the iris texture.

Iris Mask comparions

  • maskcmp … Comparison of iris masks

Iris Feature Extracation

  • lg … 1D-LogGabor Feature Extraction (=> hd for comparison)
  • cg … Complex Gabor filterbanks as used by Daugman (=> hd for comparison)
  • qsw … Extraction with the algorithm of Ma et al. (=> hd for comparison)
  • ko … Algorithm of ko et al. (=> koc for comparison)
  • cr … Algorithm of Rathgeb and Uhl (=> hd for comparison)
  • cb … Context-based Iris Recognition (=> cbc for comparison)
  • dct … Algorithm of Monroe et al. (=> dctc for comparison)
  • sift … Sift points as iris code (=> siftc for comparison)
  • surf … Surf points as iris code (=> surfc for comparison)
  • lbp … Local binary pattern based features (=> lbpcc for comparison)

Comparators

  • koc … Algorithm of Ko et al.
  • cbc … Context based algorithm
  • dctc … Algorithm of Monro et al.
  • siftc … Comparator for sift iris codes
  • surfc … Comparator for surf iris codes
  • lbpc … Comparator for lbp based iris codes
  • hd … Hamming Distance-based Comparator

Verification

  • hdverify … Performance of Hamming Distance-based verification of iris codes

Face/Face-part detection

  • gfcf … Gaussian Face and Face-part Classification Fusion

USIT packages

The packages contain software which is not part of the core USIT package. It was taken from different publications and is packaged with the USIT for convenience reasons.

Each subdirectory should be self contained and should contain the respective software as well as a readme.md which outlines the license and usage information (in case it differs from the core USIT).

Also note that windows binaries and makefiles might or might not be supplied in the packages. However, unless noted otherwise in the individual packages the requirements should be the same as for the base USIT package.

Binarized Statistical Image Features

Requirements

As base USIT and

License

The USIT License applies.

If this software is used to prepare for an article please include the following reference:

Text

Christian Rathgeb, Florian Struck, Christoph Busch, “Efficient BSIF-based Near-Infrared Iris Recognition”, in Proceedings of International Conference on Image Processing Theory, Tools and Applications (IPTA’16), 2016.

Bibtex

@INPROCEEDINGS{Rathgeb16c,
  AUTHOR     = {Christian Rathgeb and Florian Struck and Christoph Busch},
  TITLE      = {Efficient BSIF-based Near-Infrared Iris Recognition},
  BOOKTITLE  = {Proceedings of International Conference on Image Processing Theory, Tools and Applications (IPTA'16)},
  YEAR       = {2016},
}

Triple A

License

The USIT License applies.

If this software is used to prepare for an article please include the following reference:

Text

Christian Rathgeb, Heinz Hofbauer, Andreas Uhl, and Christoph Busch. “TripleA: Accelerated Accuracy-preserving Alignment for Iris-Codes”, Proceedings of the 9th IAPR/IEEE International Conference on Biometrics (ICB’16), 2016.

Bibtex

@INPROCEEDINGS{Rathgeb16b,
  AUTHOR     = {Christian Rathgeb and Heinz Hofbauer and Andreas Uhl and Christoph Busch},
  TITLE      = {{TripleA}: Accelerated Accuracy-preserving Alignment for Iris-Codes},
  BOOKTITLE  = {Proceedings of the 9th IAPR/IEEE International Conference on Biometrics (ICB'16)},
  YEAR       = {2016},
  PAGES      = {8}
}

Changelog

  • [v2.2.0] 2016.01.12
    • Fixed the bug where an empty point list for ellipse fitting would cause manuseg to break. Now the one failing input is skipped and the rest runs through.
    • HD has now the capability to report the bitshift at which the optimal HD was found.
    • Fixed a bug where the lg, cg, cr and qsw features wrote the bitsequence for iris codes bytewise out of order, i.e. byte order was correct, however bit order per byte was wrong. This lead to alignment errors with HD rotation correction with the -s option.
    • Included a package for Binarized Statistical Image Features (bsif and bsifc) from the paper:

    Christian Rathgeb, Florian Struck, Christoph Busch. “Efficient BSIF-based Near-Infrared Iris Recognition”, in Proceedings of International Conference on Image Processing Theory, Tools and Applications (IPTA’16), 2016.

  • [v2.1.0] 2016.03.22
    • Included package for TripleA from the paper:

    C. Rathgeb, H. Hofbauer, A. Uhl, and C. Busch. “TripleA: Accelerated Accuracy-preserving Alignment for Iris-Codes”, Proceedings of the 9th IAPR/IEEE International Conference on Biometrics (ICB’16), 2016.

  • [v2.0.0] 2016.02.04
    • Scaling options added as used in the paper:

    Heinz Hofbauer, Fernando Alonso-Fernandez, Josef Bigun, and Andreas Uhl. “Experimental Analysis Regarding the Influence of Iris Segmentation on the Recognition Rate,” in IET Biometrics, 2016.

    • Variable iris texture height support added as used in the paper:

    Rudolf Schraml, Heinz Hofbauer, Alexander Petutschnigg, and Andreas Uhl. “Tree Log Identification Based on Digital Cross-Section Images of Log Ends Using Fingerprint and Iris Recognition Methods,” In Proceedings of the 16th International Conference on Computer Analysis of Images and Patterns (CAIP’15), pp. 752-765, LNCS, Springer Verlag, 2015

    • New tools:
      • cg
      • lbp and lbpc
      • surf and surfc
      • sift and siftc
      • manuseg
    • Renamed iffp to ifpp (for iterative fourier push pull).

Sources and Executables

The code is available upon request.

Please fill out this form to request a download link for the software:

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