Compared to minutia-based fingerprint representations, fixed-length representations are attractive due to simple and efficient matching. However, fixed-length fingerprint representations are limited in accuracy when matching fingerprints with different visible areas, which can occur due to different finger poses or acquisition methods. To address this issue, we propose a localized deep representation of fingerprint, named LDRF. By focusing on the discriminative characteristics within local regions, LDRF provides a more robust and accurate fixed-length representation for fingerprints with variable visible areas. LDRF can be adapted to retain information within any valid area, making it highly flexible. The matching scores produced by LDRF also exhibit intuitive statistical characteristics, which led us to propose a matching score normalization technique to mitigate the uncertainty in the cases of very small overlapping area. With this new technique, we can maintain a high level of accuracy and reliability in our fingerprint matching, even as the size of the database grows rapidly. Our experimental results on 21 datasets containing over 140K fingerprints of various finger poses and impression types show that LDRF outperforms other fixed-length representations and is robust to sensing technologies and impression types. Besides, the proposed matching score normalization effectively reduces the false match rate (FMR) in large-scale identification experiments comprising over 5.11 million fingerprints. Specifically, this technique results in a reduction of two orders of magnitude compared to matching without matching score normalization and five orders of magnitude compared to prior works.

翻译：与基于细节点（minutia）的指纹表示相比，定长表示因其简洁高效匹配而具有吸引力。然而，当匹配不同可见区域（可能由不同手指姿态或采集方式导致）的指纹时，定长指纹表示的精度受限。为解决该问题，我们提出一种局部深度指纹表示方法，命名为LDRF。通过聚焦局部区域的判别性特征，LDRF为具有可变可见区域的指纹提供了一种更鲁棒且更精确的定长表示。LDRF可自适应保留任意有效区域内的信息，展现出高度灵活性。此外，LDRF产生的匹配分数具有直观的统计特性，为此我们提出一种匹配分数归一化技术，以减轻重叠区域过小情况下的不确定性。借助该新技术，即使在数据库规模快速增长的条件下，也能保持指纹匹配的高精度与高可靠性。在包含超过14万枚来自不同手指姿态与印痕类型的21个数据集上的实验结果表明，LDRF优于其他定长表示方法，并对传感技术与印痕类型具有鲁棒性。此外，所提匹配分数归一化技术在包含逾511万枚指纹的大规模识别实验中有效降低了误匹配率（FMR）。具体而言，该技术相比不使用匹配分数归一化的方法可将FMR降低两个数量级，相比先前工作则降低五个数量级。