学术文献库

Authentication and identification methods based on human fingerprints are ubiquitous in several systems ranging from government organizations to consumer products. The performance and reliability of such systems directly rely on the volume of data on which they have been verified. Unfortunately, a large volume of fingerprint databases is not publicly available due to many privacy and security concerns. In this paper, we introduce a new approach to automatically generate high-fidelity synthetic fingerprints at scale. Our approach relies on (i) Generative Adversarial Networks to estimate the probability distribution of human fingerprints and (ii) Super-Resolution methods to synthesize fine-grained textures. We rigorously test our system and show that our methodology is the first to generate fingerprints that are computationally indistinguishable from real ones, a task that prior art could not accomplish.