Synthetic Latent Fingerprint Generation Using Style Transfer

TL;DR

This paper introduces a style transfer-based method to generate realistic synthetic latent fingerprints, addressing data scarcity for training neural networks in fingerprint recognition.

Contribution

It presents a novel approach combining style transfer and image blending to produce diverse, identity-preserving synthetic latent fingerprints from contact-based inputs.

Findings

Synthetic fingerprints retain identity information.

Generated fingerprints mimic real latent fingerprint characteristics.

Method can produce multiple diverse samples from one fingerprint.

Abstract

Limited data availability is a challenging problem in the latent fingerprint domain. Synthetically generated fingerprints are vital for training data-hungry neural network-based algorithms. Conventional methods distort clean fingerprints to generate synthetic latent fingerprints. We propose a simple and effective approach using style transfer and image blending to synthesize realistic latent fingerprints. Our evaluation criteria and experiments demonstrate that the generated synthetic latent fingerprints preserve the identity information from the input contact-based fingerprints while possessing similar characteristics as real latent fingerprints. Additionally, we show that the generated fingerprints exhibit several qualities and styles, suggesting that the proposed method can generate multiple samples from a single fingerprint.