论文标题
Printsgan：合成指纹生成器
PrintsGAN: Synthetic Fingerprint Generator
论文作者
论文摘要
在指纹识别领域工作的研究人员的主要障碍是缺乏公开可用的大规模指纹数据集。确实存在的公开数据集包含很少的身份和印象。这限制了许多主题的研究，包括例如，使用深网来学习固定的长度指纹嵌入。因此，我们提出了一种printsgan，这是一种合成指纹生成器，能够生成独特的指纹以及给定指纹的多个印象。使用Printsgan，我们合成一个525K指纹的数据库（35k不同的手指，每个手指有15个印象）。接下来，我们通过训练深层网络来从指纹提取固定长度嵌入，从而显示了Printsgan生成的数据集的实用性。特别是，一种嵌入模型对我们的合成指纹训练，并在少数公开可用的真实指纹（NIST SD302的25K印刷品）上进行了微调，获得了87.03％ @ far = 0.01％的TAR = 0.01％，NIST SD4数据库（仅在TAR = 73.37％的bar = 73.37％时，NIST SD4 Database（仅在tar = 73.37％）上训练NIST n n nist sd302 nists sd nist sd nists inSIST sd nists nists insist inSist inSIST inSIST inSIST inSIST inSIST inSIST inSISTSD 302。由于i）缺乏现实主义或II）无法每只手指产生多种印象，因此，盛行的合成指纹生成方法不能使这种性能提高。我们计划向公众发布合成指纹的数据库。
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.
