One of the most challenging problems in fingerprint recognition continues to be establishing the identity of a suspect associated with partial and smudgy fingerprints left at a crime scene (i.e., latent prints or fingermarks). Despite the success of fixed-length embeddings for rolled and slap fingerprint recognition, the features learned for latent fingerprint matching have mostly been limited to local minutiae-based embeddings and have not directly leveraged global representations for matching. In this paper, we combine global embeddings with local embeddings for state-of-the-art latent to rolled matching accuracy with high throughput. The combination of both local and global representations leads to improved recognition accuracy across NIST SD 27, NIST SD 302, MSP, MOLF DB1/DB4, and MOLF DB2/DB4 latent fingerprint datasets for both closed-set (84.11%, 54.36%, 84.35%, 70.43%, 62.86% rank-1 retrieval rate, respectively) and open-set (0.50, 0.74, 0.44, 0.60, 0.68 FNIR at FPIR=0.02, respectively) identification scenarios on a gallery of 100K rolled fingerprints. Not only do we fuse the complimentary representations, we also use the local features to guide the global representations to focus on discriminatory regions in two fingerprint images to be compared. This leads to a multi-stage matching paradigm in which subsets of the retrieved candidate lists for each probe image are passed to subsequent stages for further processing, resulting in a considerable reduction in latency (requiring just 0.068 ms per latent to rolled comparison on a AMD EPYC 7543 32-Core Processor, roughly 15K comparisons per second). Finally, we show the generalizability of the fused representations for improving authentication accuracy across several rolled, plain, and contactless fingerprint datasets.

翻译：指纹识别中最具挑战性的问题之一，依然是识别与犯罪现场遗留的部分模糊指纹（即潜指纹或指痕）相关的嫌疑人身份。尽管固定长度嵌入在全指纹和按压指纹识别中取得了成功，但用于潜指纹匹配的特征学习大多局限于基于局部细节点的嵌入，并未直接利用全局表示进行匹配。本文结合全局嵌入与局部嵌入，实现了高吞吐量的最先进潜指纹到全指纹匹配精度。局部与全局表示的融合，在包含10万枚全指纹样本库的闭集识别场景下（NIST SD 27、NIST SD 302、MSP、MOLF DB1/DB4、MOLF DB2/DB4潜指纹数据集上的rank-1检索率分别为84.11%、54.36%、84.35%、70.43%、62.86%）和开集识别场景下（FPIR=0.02时FNIR分别为0.50、0.74、0.44、0.60、0.68）均提升了识别精度。我们不仅融合了互补表示，还利用局部特征引导全局表示聚焦于待比较两幅指纹图像中的判别性区域。这形成了一种多阶段匹配范式：每个查询图像检索候选列表的子集被传递至后续阶段进行进一步处理，从而显著降低延迟（在AMD EPYC 7543 32核处理器上每次潜指纹到全指纹比较仅需0.068毫秒，约每秒完成1.5万次比较）。最后，我们展示了融合表示在多个全指纹、按压指纹及非接触式指纹数据集上提升认证精度的泛化能力。