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.

本研究旨在解决潜在指纹领域中数据稀缺的问题，通过样式迁移和图像混合的方法，合成逼真的潜在指纹，其生成的合成指纹在保留输入接触式指纹的身份信息的同时，具有类似真实潜在指纹的特征，且能够生成多个样本。

使用风格迁移生成合成潜在指纹

Synthetic Latent Fingerprint Generation Using Style Transfer

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 exist contain very few
identities and impressions per finger. This limits research on a number of
topics, including e.g., using deep networks to learn fixed length fingerprint
embeddings. Therefore, we propose PrintsGAN, a synthetic fingerprint generator
capable of generating unique fingerprints along with multiple impressions for a
given fingerprint. Using PrintsGAN, we synthesize a database of 525k
fingerprints (35K distinct fingers, each with 15 impressions). Next, we show
the utility of the PrintsGAN generated dataset by training a deep network to
extract a fixed-length embedding from a fingerprint. In particular, an
embedding model trained on our synthetic fingerprints and fine-tuned on a small
number of publicly available real fingerprints (25K prints from NIST SD302)
obtains a TAR of 87.03% @ FAR=0.01% on the NIST SD4 database (a boost from
TAR=73.37% when only trained on NIST SD302). Prevailing synthetic fingerprint
generation methods do not enable such performance gains due to i) lack of
realism or ii) inability to generate multiple impressions per finger. We plan
to release our database of synthetic fingerprints to the public.

我们提出了 PrintsGAN，这是一种合成指纹生成器，能够为给定的指纹生成独特的指纹和多个印象。使用 PrintsGAN，我们合成了一个由 525k 个指纹（包括 15 个印象的 35k 个不同手指）组成的数据库，利用该数据库，我们训练了一个深度网络，从指纹中提取了一个固定长度的嵌入，这在真实情况下是非常困难的。