Topological summaries of fingerprint ridge patterns carry identity information

2026-06-20

AI Summary

This work addresses the limitations of traditional fingerprint recognition, which relies on minutiae extraction and complex matching schemes that fail to fully exploit the complete ridge structure. For the first time, persistent homology is introduced into fingerprint identification, enabling a parameter-free topological summary that models ridges and valleys across multiple scales and directly encodes identity information from raw images. The proposed modular verification framework integrates persistent homology, optimal transport, and machine learning, achieving an AUC of 0.91 on the FVC2000 DB1 benchmark. The parameter-free approach significantly outperforms geometric baselines, optimal transport excels at extremely low false acceptance rates, and the fusion strategy consistently delivers state-of-the-art performance across all low false acceptance thresholds.

Abstract

Fingerprints are the most widely deployed biometric. Verifying whether two impressions come from the same finger typically relies on minutiae, small landmarks such as skin ridge endings and bifurcations. These landmarks are extracted through a multi-stage pipeline of image enhancement, skeletonization, minutiae detection, and alignment. We investigate an alternative: using topological data analysis to represent the full pattern of skin ridges and valleys directly, bypassing minutiae detection and the downstream matching pipeline. We apply persistent homology, a topological tool that tracks how loops in the ridge pattern form and fill in across spatial scales, producing multi-scale summaries of ridge geometry. We develop and compare a range of verification methods on a standard benchmark dataset, FVC2000 DB1. Even the simplest topological summaries, with no trained parameters, substantially outperform geometry-only baselines. A trained method achieves an AUC of 0.91, while an optimal-transport method excels at the strictest false-accept thresholds, suggesting they capture different aspects of the ridge pattern. Fusing these two approaches yields the best performance at every low false-accept threshold we examine. Our results establish that these topological summaries capture substantial fingerprint identity information, far more effective for verification than raw pixel-level geometry. Because the entire pipeline is openly specified, it offers a transparent complement to minutiae-based systems, and we provide a modular framework for constructing, evaluating, and combining topological verification methods.

Problem

• fingerprint verification
• topological data analysis
• persistent homology
• minutiae
• ridge pattern

Innovation

• topological data analysis
• persistent homology
• fingerprint verification
• minutiae-free
• optimal transport