We introduce a high-performance fingerprint liveness feature extraction
technique that secured first place in LivDet 2023 Fingerprint Representation
Challenge. Additionally, we developed a practical fingerprint recognition
system with 94.68% accuracy, earning second place in LivDet 2023 Liveness
Detection in Action. By investigating various methods, particularly style
transfer, we demonstrate improvements in accuracy and generalization when faced
with limited training data. As a result, our approach achieved state-of-the-art
performance in LivDet 2023 Challenges.

我们介绍了一种高性能的指纹活体特征提取技术，该技术在 LivDet 2023 指纹特征展示挑战中获得第一名。此外，我们还开发了一个实用的指纹识别系统，准确率达到 94.68%，在 LivDet 2023 活体检测行动中获得第二名。通过研究各种方法，特别是样式转移，我们展示了在面对有限的训练数据时准确性和泛化性的提升。因此，我们的方法在 LivDet 2023 挑战中实现了最先进的性能。

自适应样式技术提升指纹活体检测的泛化能力

Boosting Generalization with Adaptive Style Techniques for Fingerprint  Liveness Detection

One of the open issues in fingerprint verification is the lack of robustness
against image-quality degradation. Poor-quality images result in spurious and
missing features, thus degrading the performance of the overall system.
Therefore, it is important for a fingerprint recognition system to estimate the
quality and validity of the captured fingerprint images. In this work, we
review existing approaches for fingerprint image-quality estimation, including
the rationale behind the published measures and visual examples showing their
behavior under different quality conditions. We have also tested a selection of
fingerprint image-quality estimation algorithms. For the experiments, we employ
the BioSec multimodal baseline corpus, which includes 19200 fingerprint images
from 200 individuals acquired in two sessions with three different sensors. The
behavior of the selected quality measures is compared, showing high correlation
between them in most cases. The effect of low-quality samples in the
verification performance is also studied for a widely available minutiae-based
fingerprint matching system.

该研究论文研究了指纹验证中的一个问题：指纹图像的质量下降不利于系统的整体性能，因此，为了解决这个问题，需要对定捕获的指纹图像进行质量和有效性的估计。