Revisiting Handcrafted Minutiae Detection: A Simple and Effective Open Source Baseline for Modern Fingerprint Workflows

2026-10-08

AI Summary

This study addresses the limitations of traditional open-source fingerprint feature extraction algorithms, which suffer from outdated codebases and poor integration with modern scientific computing ecosystems. To this end, we propose SBMEX and integrate it into the pyfing library. The method introduces a dual lookup table architecture to replace runtime neighborhood scanning, coupled with a continuous quality scoring framework. By leveraging skeletonization, dual ridge-valley fusion, and spatial density attenuation techniques, SBMEX achieves fast and deterministic minutiae detection. Evaluations on NIST datasets demonstrate that SBMEX matches or exceeds baseline accuracy without requiring fine-tuning while significantly reducing detection latency. Ultimately, this work provides an efficient, accessible, and modernized solution for fingerprint recognition within contemporary computational workflows.

Abstract

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 However, current open-source traditional baselines are severely outdated, relying almost exclusively on legacy C/C++ codebases that lack seamless integration with modern scientific software ecosystems.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.

Problem

• Minutiae Detection
• Fingerprint
• Open Source Baseline
• Biometrics

Innovation

• Minutiae Detection
• Dual Look-Up Table
• Crossing Number
• Quality Scoring
• Skeleton Tracking