发表机构
University of Bologna（博洛尼亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出集成于开源 pyfing 包的 SBMEX 方法，通过双查找表架构等优化，在 NIST SD302 数据集上实现了高精度、低延迟的 minutiae 检测，为现代指纹工作流提供了有效开源基线。
AI 中文摘要
手工 minutiae 检测算法由于具备完全可审计性、符合国际标准、不依赖训练数据集或 GPU 硬件等特性，仍然是生物识别科学和法医实践的基础。然而，当前的开源传统基线严重过时，几乎完全依赖遗留的 C/C++ 代码库，无法与现代科学软件生态系统无缝集成。为了弥合这一差距，本研究提出了 SBMEX（基于骨架的 minutiae 提取，Skeleton-Based Minutiae EXtraction），这是一种快速且确定性的 minutiae 检测方法，集成到开源 pyfing 包中。SBMEX 采用双查找表架构，替代了 Crossing Number 计算和骨架跟踪过程中的运行时邻域扫描，从而实现了高计算吞吐量。此外，它还包含一个连续质量评分框架，该框架由跟踪路径长度、双脊谷骨架融合和空间密度衰减驱动。在 NIST SD302 数据集上的严格评估表明，SBMEX 无需微调即可达到与传统开源基线相当或更优的特征提取准确率，同时相对于经典的 Crossing Number Python 实现，minutiae 检测延迟大幅降低。
英文摘要
Handcrafted minutiae detection algorithms remain fundamental to biometric science and forensic practice due to their full auditability, adherence to international standards, and operational independence from training datasets or GPU hardware. However, current open-source traditional baselines are severely outdated, relying almost exclusively on legacy C/C++ codebases that lack seamless integration with modern scientific software ecosystems. To bridge this gap, the present work introduces SBMEX (Skeleton-Based Minutiae EXtraction), a fast and deterministic minutiae detection method integrated into the open source \texttt{pyfing} package. SBMEX achieves high computational throughput by employing a dual Look-Up Table architecture that replaces runtime neighborhood scanning during Crossing Number computation and skeleton tracking. Additionally, it incorporates a continuous quality scoring framework driven by tracking path length, dual ridge-valley skeleton fusion, and spatial density decay. Rigorous evaluation on NIST SD302 datasets demonstrates that SBMEX delivers feature extraction accuracy comparable to or outperforming traditional open-source baselines without fine-tuning, while achieving a drastic reduction in minutiae detection latency relative to classical Crossing Number Python implementations.
