Tools for the Generation of Synthetic Biometric Sample Data (GENSYNTH)
Tools for the Generation of Synthetic Biometric Sample Data (FWF project I4272) is a joint DFG/FWF bilateral project together with
Jana Dittmanns group at the University of Magdeburg
which researches methods allowing for the generation of large-scale sets of plausible and realistic synthetic biometric data to enable reproducible, flexible and timely biometric and forensic experimental assessments, not only compliant with the hunger for data we see with modern day techniques, but also with EU data protection legislation.
To achieve our goals, the work in this project follows two distinct solution approaches: The first (data adaptation) takes existing biometric / forensic samples, adapts them to reflect certain acquisition conditions (sensorial, physiological as well as environmental variability), and (if required by the application context) conducts context sensitive control of privacy attributes. The second approach (synthesizing) creates completely artificial samples from scratch according to specified sensorial, physiological as well as environmental variability.
The practical work in the project is focused on digitized forensic (latent) fingerprints as well as on the two biometric modalities fingerprint (FP) and vascular data of hand and fingers (i.e. hand- and finger-vein images) (HFV). The theoretical and methodological concepts and empirical findings will be generalized, to discuss the potential benefits of the research performed also for other modalities (esp. in face recognition).
- [Drozdowski20a ] Demographic Bias: A Challenge for Fingervein Recognition Systems? In Proceedings of the 2020 European Signal Processing Conference (EUSIPCO'20), pp. 1-6, Amsterdam, The Nederlands, 2020, accepted
- [Debiasi20a ] Presentation Attacks and Detection in Finger- and Hand-Vein Recognition In Proceedings of the Joint Austrian Computer Vision and Robotics Workshop (ACVRW'20), pp. 65-70, Graz, Austria, September 17 - September 18