PrintsGAN：合成指纹生成器
PrintsGAN: Synthetic Fingerprint Generator
摘要：
研究人员在指纹识别领域工作的一个主要障碍是缺乏公开的大规模指纹数据集。公开可用的数据集确实存在，每个手指包含的身份和印象非常少。这限制了对许多主题的研究，例如，使用深度网络来学习固定长度的指纹嵌入。因此，我们提出PrintsGAN，一个合成指纹生成器，能够生成独特的指纹以及给定指纹的多个印象。使用PrintsGAN，我们合成了一个包含525 k个指纹的数据库（35 k个不同的手指，每个手指有15个印记）。接下来，我们通过训练深度网络来从指纹中提取固定长度的嵌入，展示PrintsGAN生成的数据集的实用性。特别是，在我们的合成指纹上训练并在少量公开可用的真实指纹（来自NIST SD 302的25 K打印）上进行微调的嵌入模型在NIST SD 4数据库上获得了87.03%@FAR =0.01%的TAR（仅在NIST SD 302上训练时，TAR=73.37%）。流行的合成指纹生成方法由于i）缺乏真实性或ii）不能每个手指生成多个印记而不能实现这样的性能增益。我们计划向公众公布我们的合成指纹数据库。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
