Alexander Spenke
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Alexander Spenke has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Bernhard Kohn
Department of Digital Safety and Security, Austrian Institute of Technology, 1210 Vienna, Austria
1 shared publication - Clemens Heitzinger
TU Wien
1 shared publication - Gerd Nolden
Federal Office for Information Security, Bundesamt für Sicherheit in der Informationstechnik, 53175 Bonn, Germany
1 shared publication - Laurenz Ruzicka
Department of Digital Safety and Security, Austrian Institute of Technology, 1210 Vienna, Austria
1 shared publication - Stephan Bergmann
Federal Office for Information Security, Bundesamt für Sicherheit in der Informationstechnik, 53175 Bonn, Germany
1 shared publication
Latest Publications
- Open Access
Towards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach
Fingerprint mosaicking—the process of combining multiple fingerprint impressions into a single master fingerprint—is an essential step in modern biometric systems, but it is prone to errors that can significantly degrade image quality. This paper proposes a deep learning-based approach to detect and score hard mosaicking artifacts in fingerprint images. Our method uses a self-supervised learning framework to train a segmentation model on large-scale unlabeled fingerprint data, eliminating the need for manual artifact annotation. The proposed model effectively identifies mosaicking errors, achieving high segmentation performance across multiple fingerprint modalities—contactless, rolled, and pressed—and proves robust to different data sources. We also introduce a mosaicking artifact score that quantifies the severity of detected errors and enables automated evaluation of fingerprint images at scale. Training and evaluation rely on synthetic artifacts, we therefore provide a qualitative comparison to real stitching failures and discuss the limits of this validation strategy in detail. By addressing the previously underexplored problem of reference-free hard-artifact detection in fingerprints, our work contributes to improving the accuracy and reliability of fingerprint-based biometric systems.
Open AccessTowards Fingerprint Mosaicking Artifact Detection: A Self-Supervised Deep Learning Approach
Fingerprint mosaicking—the process of combining multiple fingerprint impressions into a single master fingerprint—is an essential step in modern biometric systems, but it is prone to errors that can significantly degrade image quality. This paper proposes a deep learning-based approach to detect and score hard mosaicking artifacts in fingerprint images. Our method uses a self-supervised learning framework to train a segmentation model on large-scale unlabeled fingerprint data, eliminating the need for manual artifact annotation. The proposed model effectively identifies mosaicking errors, achieving high segmentation performance across multiple fingerprint modalities—contactless, rolled, and pressed—and proves robust to different data sources. We also introduce a mosaicking artifact score that quantifies the severity of detected errors and enables automated evaluation of fingerprint images at scale. Training and evaluation rely on synthetic artifacts, we therefore provide a qualitative comparison to real stitching failures and discuss the limits of this validation strategy in detail. By addressing the previously underexplored problem of reference-free hard-artifact detection in fingerprints, our work contributes to improving the accuracy and reliability of fingerprint-based biometric systems.