Enhancing Fingerprint Image Synthesis with GANs, Diffusion Models, and Style Transfer Techniques

arXiv:2403.13916v1 Announce Type: cross
Abstract: We present novel approaches involving generative adversarial networks and diffusion models in order to synthesize high quality, live and spoof fingerprint images while preserving features such as uniqueness and diversity. We generate live fingerprints from noise with a variety of methods, and we use image translation techniques to translate live fingerprint images to spoof. To generate different types of spoof images based on limited training data we incorporate style transfer techniques through a cycle autoencoder …

abstract adversarial arxiv cs.cv cs.lg diffusion diffusion models diversity features fingerprints gans generate generative generative adversarial networks image images networks noise novel quality style style transfer synthesis transfer type