General Requirements on Synthetic Fingerprint Images for Biometric Authentication and Forensic Investigations

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.