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：相较于基于细节特征的指纹表示方法，固定长度表示因其简单高效的匹配特性而备受青睐。然而，当匹配具有不同可见区域的指纹时（可能由不同手指姿态或采集方法导致），固定长度指纹表示在准确性方面存在局限。为解决此问题，我们提出一种名为LDRF的局部深度指纹表示方法。通过聚焦局部区域内的判别性特征，LDRF为具有可变可见区域的指纹提供了更鲁棒、更准确的固定长度表示。LDRF可适配保留任意有效区域内的信息，具有高度灵活性。LDRF生成的匹配分数还展现出直观的统计特性，这促使我们提出一种匹配分数归一化技术，以减轻重叠区域极小时的匹配不确定性。借助这项新技术，即使在数据库规模快速增长的情况下，我们仍能保持指纹匹配的高精度与高可靠性。我们在包含14万枚以上不同手指姿态和按压类型指纹的21个数据集上的实验结果表明，LDRF优于其他固定长度表示方法，且对传感技术和按压类型具有鲁棒性。此外，在包含超过511万枚指纹的大规模识别实验中，所提出的匹配分数归一化技术有效降低了错误匹配率（FMR）。具体而言，该技术相较于未使用匹配分数归一化的方法降低了两个数量级的错误率，相较于现有工作降低了五个数量级。