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.

与图纹型指纹表示相比，固定长度的表示因其简单和高效的匹配而具有吸引力。然而，当匹配不同可见区域的指纹时，固定长度的指纹表示在准确度上存在限制。为了解决这个问题，我们提出了一种名为 LDRF 的局部深度指纹表示。LDRF 通过关注局部区域内的区分特征，为具有不同可见区域的指纹提供了更加稳健和准确的固定长度表示。LDRF 可以适应保留在任何有效区域内的信息，具有高度的灵活性。LDRF 产生的匹配分数还展现出直观的统计特性，因此我们提出了一种匹配分数归一化技术来减轻在非常小的重叠区域情况下的不确定性。通过这种新技术，即使数据库规模迅速增长，我们仍能保持高水准的准确性和可靠性。我们在 21 个数据集上进行的实验结果包含超过 140K 个具有不同手指姿势和印记类型的指纹，表明 LDRF 优于其他固定长度表示，并且对感测技术和印记类型具有鲁棒性。此外，所提出的匹配分数归一化技术在包含超过 5.11 百万个指纹的大规模识别实验中有效降低了错误匹配率（FMR），相较于不进行匹配分数归一化和之前的研究，降低了两个数量级和五个数量级。