Deep learning-based models have been shown to improve the accuracy of fingerprint
recognition. While these algorithms show exceptional performance, they require large-scale …

Generating realistic biometric images has been an interesting and, at the same time,
challenging problem. Classical statistical models fail to generate realistic-looking fingerprint …

Real biometric fingerprint samples belong to the category of personal data, and therefore
their usage for deep learning model training may have certain limitations. Artificially …

Artificial fingerprint synthesis can broaden the opportunity for researchers by generating a
large number of realistic synthetic fingerprints without having to worry about legal issues or …

We present novel approaches involving generative adversarial networks and diffusion
models in order to synthesize high quality, live and spoof fingerprint images while …

Training fingerprint recognition models using synthetic data has recently gained increased
attention in the biometric community as it alleviates the dependency on sensitive personal …

Generating fingerprint images for biometric purposes is both necessary and challenging. In
this study, we presented a fingerprint generation approach based on generative adversarial …

A major impediment to researchers working in the area of fingerprint recognition is the lack
of publicly available, large-scale, fingerprint datasets. The publicly available datasets that do …

Authentication and identification methods based on human fingerprints are ubiquitous in
several systems ranging from government organizations to consumer products. The …

We present two related methods for creating MasterPrints, synthetic fingerprints that a
fingerprint verification system identifies as many different people. Both methods start with …