Tools for the Generation of Synthetic Biometric Sample Data

Current day biometric recognition and digitized forensics research struggles with a problem severely impeding progress in these security relevant fields: Large scale datasets of biometric data would be required to allow for flexible and timely assessments, but these are missing due to various reasons, amongst them privacy concerns. The latter have increased with the EU GDPR to an extend that even well established standardization bodies like NIST in the USA removed a large part of their publically available datasets before the GDPR became effective in May 2018. To solve this problem and address the attached data quality dimensions (quantitative as well as qualitative concerns), we will research methods allowing for the generation of large-scale sets of plausible and realistic synthetic 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).

Current day biometric recognition and digitized forensics research struggles with a problem severely impeding progress in these security relevant fields: Large scale datasets of biometric data would be required to allow for flexible and timely assessments, but these are missing due to various reasons, amongst them privacy concerns. The latter have increased with the EU GDPR to an extend that even well established standardization bodies like NIST in the USA removed a large part of their publically available datasets before the GDPR became effective in May 2018. To solve this problem and address the attached data quality dimensions (quantitative as well as qualitative concerns), we will research methods allowing for the generation of large-scale sets of plausible and realistic synthetic 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).