Minutiae-Anchored Local Dense Representation for Fingerprint Matching

TL;DR

This paper introduces DMD, a minutiae-anchored local dense representation for fingerprint matching, capturing detailed ridge textures and minutiae features to improve accuracy and robustness across diverse fingerprint datasets.

Contribution

The paper presents a novel minutiae-anchored local dense descriptor that enhances fingerprint matching by integrating ridge textures and minutiae features in a spatially structured manner.

Findings

Achieves state-of-the-art accuracy on multiple fingerprint benchmarks.

Demonstrates robustness across diverse fingerprint capture conditions.

Maintains high computational efficiency for large-scale recognition.

Abstract

Fingerprint matching under diverse capture conditions remains a fundamental challenge in biometric recognition. To achieve robust and accurate performance in such scenarios, we propose DMD, a minutiae-anchored local dense representation which captures both fine-grained ridge textures and discriminative minutiae features in a spatially structured manner. Specifically, descriptors are extracted from local patches centered and oriented on each detected minutia, forming a three-dimensional tensor, where two dimensions represent spatial locations on the fingerprint plane and the third encodes semantic features. This representation explicitly captures abstract features of local image patches, enabling a multi-level, fine-grained description that aggregates information from multiple minutiae and their surrounding ridge structures. Furthermore, thanks to its strong spatial correspondence with the patch image, DMD allows for the use of foreground segmentation masks to identify valid descriptor regions. During matching, comparisons are then restricted to overlapping foreground areas, improving efficiency and robustness. Extensive experiments on rolled, plain, parital, contactless, and latent fingerprint datasets demonstrate the effectiveness and generalizability of the proposed method. It achieves state-of-the-art accuracy across multiple benchmarks while maintaining high computational efficiency, showing strong potential for large-scale fingerprint recognition. Corresponding code is available at https://github.com/Yu-Yy/DMD.