no code implementations • 18 Apr 2024 • Robert Jöchl, Andreas Uhl
In a previous work, we have shown that the presence of strong in-field sensor defects is irrelevant for a CNN to predict the age class.
no code implementations • 2 Nov 2023 • Eduardo Ribeiro, Andreas Uhl, Fernando Alonso-Fernandez, Reuben A. Farrugia
In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem.
no code implementations • 3 Oct 2023 • Robert Jöchl, Andreas Uhl
In the context of temporal image forensics, it is not evident that a neural network, trained on images from different time-slots (classes), exploits solely image age related features.
no code implementations • 5 Mar 2023 • Christian Rathgeb, Jascha Kolberg, Andreas Uhl, Christoph Busch
Biometric systems utilising deep learning have been shown to achieve auspicious recognition accuracy, surpassing human performance.
no code implementations • 20 Feb 2023 • Simon Kirchgasser, Christof Kauba, Georg Wimmer, Andreas Uhl
Natural Scene Statistics commonly used in non-reference image quality measures and a deep learning based quality assessment approach are proposed as biometric quality indicators for vasculature images.
no code implementations • 10 Nov 2022 • Heinz Hofbauer, Fernando Alonso-Fernandez, Josef Bigun, Andreas Uhl
In this study the authors will look at the detection and segmentation of the iris and its influence on the overall performance of the iris-biometric tool chain.
no code implementations • 24 Oct 2022 • Eduardo Ribeiro, Andreas Uhl, Fernando Alonso-Fernandez
The use of low-resolution images adopting more relaxed acquisition conditions such as mobile phones and surveillance videos is becoming increasingly common in iris recognition nowadays.
no code implementations • 20 Oct 2022 • Eduardo Ribeiro, Andreas Uhl, Fernando Alonso-Fernandez
Several recent works have addressed the ability of deep learning to disclose rich, hierarchical and discriminative models for the most diverse purposes.
no code implementations • 16 Mar 2022 • Johannes Schuiki, Michael Linortner, Georg Wimmer, Andreas Uhl
The last decade has brought forward many great contributions regarding presentation attack detection for the domain of finger and hand vein biometrics.
1 code implementation • 2 Mar 2021 • Babak Maser, Andreas Uhl
We study the finger vein (FV) sensor model identification task using a deep learning approach.
1 code implementation • 8 Feb 2021 • Babak Maser, Andreas Uhl
Identifying the origin of a sample image in biometric systems can be beneficial for data authentication in case of attacks against the system and for initiating sensor-specific processing pipelines in sensor-heterogeneous environments.
no code implementations • 12 Jan 2021 • Georg Wimmer, Rudolf Schraml, Heinz Hofbauer, Alexander Petutschnigg, Andreas Uhl
The proof of origin of logs is becoming increasingly important.
no code implementations • 1 Dec 2020 • Christof Kauba, Luca Debiasi, Andreas Uhl
In this work we evaluate five different commercial-off-the-shelf fingerprint scanners based on different sensing technologies, including optical, optical multispectral, passive capacitive, active capacitive and thermal regarding their susceptibility to presentation attacks using fake fingerprint representations.
no code implementations • 27 Apr 2020 • Georg Wimmer, Michael Gadermayr, Andreas Vécsei, Andreas Uhl
We investigate if models can be trained on virtual (or a mixture of virtual and real) samples to improve overall accuracy in a setting with limited labeled training data.
4 code implementations • NeurIPS 2017 • Christoph Hofer, Roland Kwitt, Marc Niethammer, Andreas Uhl
Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems.
no code implementations • 30 Apr 2015 • Sebastian Hegenbart, Roland Kwitt, Andreas Uhl
The 39th annual workshop of the Austrian Association for Pattern Recognition (OAGM/AAPR) provides a platform for presentation and discussion of research progress as well as research projects within the OAGM/AAPR community.