Fast and reliable fingerprint feature detection for modern workflows
Revisiting Handcrafted Minutiae Detection: A Simple and Effective Open Source Baseline for Modern Fingerprint Workflows
Computer Vision and Pattern Recognition
Summary
Fingerprint recognition relies on detecting tiny details called minutiae to identify people. The authors developed a new method named SBMEX that finds these details quickly and consistently without needing complex hardware or training data. It integrates smoothly with modern software tools and performs as well or better than older methods, while being faster and easier to use. This makes fingerprint analysis more accessible and efficient for forensic and biometric applications.
What this means in practice
- •For biometric system developers: Implement fingerprint feature extraction that is fast, deterministic, and easily integrated into current scientific software stacks.
- •For forensic software engineers: Use an accurate, open-source fingerprint minutiae detector that respects international standards and requires no GPU or training data.
Authors
Raffaele Cappelli
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 However, current open-source traditional baselines are severely outdated, relying almost exclusively on legacy C/C++ codebases that lack seamless integration with modern scientific software ecosystems.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.