Please use this identifier to cite or link to this item:
Title: General requirements on synthetic fingerprint images for biometric authentication and forensic investigations
Author(s): Makrushin, AndreyLook up in the Integrated Authority File of the German National Library
Kauba, Christof
Kirchgasser, Simon
Seidlitz, Stefan
Krätzer, Christian
Uhl, Andreas
Dittmann, JanaLook up in the Integrated Authority File of the German National Library
Issue Date: 2021
Type: Conference object
Language: English
URN: urn:nbn:de:gbv:ma9:1-1981185920-881306
Subjects: Biometrics
Synthetic samples
Abstract: Generation of synthetic biometric samples such as, for instance, fingerprint images gains more and more importance especially in view of recent cross-border regulations on security of private data. The reason is that biometric data is designated in recent regulations such as the EU GDPR as a special category of private data, making sharing datasets of biometric samples hardly possible even for research purposes. The usage of fingerprint images in forensic research faces the same challenge. The replacement of real datasets by synthetic datasets is the most advantageous straightforward solution which bears, however, the risk of generating “unrealistic” samples or “unrealistic distributions” of samples which may visually appear realistic. Despite numerous efforts to generate high-quality fingerprints, there is still no common agreement on how to define “high-quality” and how to validate that generated samples are realistic enough. Here, we propose general requirements on synthetic biometric samples (that are also applicable for fingerprint images used in forensic application scenarios) together with formal metrics to validate whether the requirements are fulfilled. Validation of our proposed requirements enables establishing the quality of a generative model (informed evaluation) or even the quality of a dataset of generated samples (blind evaluation). Moreover, we demonstrate in an example how our proposed evaluation concept can be applied to a comparison of real and synthetic datasets aiming at revealing if the synthetic samples exhibit significantly different properties as compared to real ones.
Open Access: Open access publication
Sponsor/Funder: Transformationsvertrag
Appears in Collections:Fakultät für Informatik (OA)

Files in This Item:
File Description SizeFormat 
Makrushin et al._General rquirements_2021.pdfZweitveröffentlichung1.85 MBAdobe PDFThumbnail