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Project Description

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.

Advanced Methods and Applications for Fingervein Recognition (FV2.0)


Project Description:

Advanced Methods and Applications for Fingervein Recognition (FWF project P32201) is a project aiming to develop a set of second generation fingervein sensors with corresponding biometrric recognition software to improve over todays‘ systems. In particular, we will develop (i) two variants of contact-less sensors allowing to acquire five fingers concurrently and (ii) two variants of sensors allowing to capture fingervein related imagery in 3-D. With the latter data, we will develop techniques for reconstructing 3-D vessel structures and will develop corresponding techniques for feature extraction and template comparisons. The availability of 3-D sensing also changes the game in presentation attacks, aka sensor spoofing, as current techniques for conducting presentation attacks against these systems rely on 2-D printout vessel structures. We will (i) show that 3-D sensing cannot be fooled using this type of artifacts and (ii) develop 3-D presentation attack artifacts to test if these can be used to fool the developed 3-D sensors. Finally, we will establish presentation attack detection techniques based on detecting blood flow in NIR-imagery (as opposed to using global motion as done in current techniques), which will also be able to detect the developed 3-D artifacts. The project will also investigate if fingervein recognition can be used in a forensic post-mortem victim identification scenario. For this purpose, the devloped sensors will be used to acquire post-mortem imagery from corpses in increasing time-steps after death to investigate the feasibility of this approach. Besides using biometric sensors, classical medical image acquisition will be used as a baseline (i.e. magnetic resonance angiography MRA) and techniques will be developed to conduct biometric comparisons among the different sensor types.





  • Sep 2019 - Aug 2022


  1. [Drozdowski20a   ] Demographic Bias: A Challenge for Fingervein Recognition Systems? Pawel Drozdowski, Bernhard Prommegger, Georg Wimmer, Rudolf Schraml, Christian Rathgeb, Andreas Uhl, Christoph Busch In Proceedings of the 2020 European Signal Processing Conference (EUSIPCO'20), pp. 1-6, Amsterdam, The Nederlands, 2020, accepted
  2. [Shekhawat20a  ] Efficient Fingervein Sample Image Encryption Sanjay Shekhawat, Heinz Hofbauer, 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, Shortlisted for best paper award
  3. [Kirchgasser19d   ] Finger Vein Template Protection based on Alignment-Robust Feature Description and Index-of-Maximum Hashing Simon Kirchgasser, Christof Kauba, Yen-Lung Lai, Jin Zhe, Andreas Uhl IEEE Transactions on Biometrics, Behavior, and Identity Science, pp. 1-12, 2020, accepted
  4. [Wimmer20a ] Finger Vein Recognition and Intra-Subject Similarity Evaluation of Finger Veins using the CNN Triplet Loss Georg Wimmer, Bernhard Prommegger, Andreas Uhl In Proceedings of the 25th International Conference on Pattern Recognition (ICPR), pp. 1-7, 2020, accepted
  5. [Uhl20a ] Venen Biometrie -- Stand der Technik Andreas Uhl Datenschutz und Datensicherheit 1, pp. 16-22, 2020
  6. [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
  7. [Kauba19c   ] Combined Fully Contactless Finger and Hand Vein Capturing Device with a Corresponding Dataset Christof Kauba, Bernhard Prommegger, Andreas Uhl Sensors 19(22):5014, pp. 1-25, 2019