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：相较于基于细节点的指纹表征，固定长度表征因其匹配简单高效而具有吸引力。然而，当匹配不同可见区域的指纹（可能因手指姿态或采集方式不同导致）时，固定长度表征在精度上存在局限。为解决此问题，我们提出了一种局部深度指纹表征方法，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更鲁棒、更精确的固定长度表征。LDRF可自适应保留任意有效区域内的信息，具有高度灵活性。由LDRF生成的匹配分数还呈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，以减轻极小重叠区域情况下的不确定性。借助该新技术，即使数据库规模快速增长，我们也能在指纹匹配中保持高精度和高可靠性。我们在包含超过14万枚不同手指姿态和捺印类型指纹的21个数据集上的实验结果表明，LDRF优于其他固定长度表征，并对传感技术和捺印类型具有鲁棒性。此外，所提出的匹配分数归一化技术在包含超过511万枚指纹的大规模识别实验中有效降低了错误匹配率（FMR）。具体而言，与未使用匹配分数归一化的方法相比，该技术将FMR降低两个数量级；与先前工作相比，则降低五个数量级。