Fixed-length Dense Descriptor for Efficient Fingerprint Matching

TL;DR

This paper introduces a 3D fixed-length dense descriptor (FDD) that improves fingerprint matching efficiency and robustness, especially with partial, noisy, or cross-modal fingerprints, outperforming existing descriptors.

Contribution

The paper proposes a novel 3D fixed-length dense descriptor (FDD) that captures spatial relationships, enhancing robustness and interpretability in fingerprint matching tasks.

Findings

FDD outperforms existing fixed-length descriptors in various datasets.

FDD is effective for partial, cross-modal, and noisy fingerprint matching.

FDD demonstrates superior robustness and spatial interpretability.

Abstract

In fingerprint matching, fixed-length descriptors generally offer greater efficiency compared to minutiae set, but the recognition accuracy is not as good as that of the latter. Although much progress has been made in deep learning based fixed-length descriptors recently, they often fall short when dealing with incomplete or partial fingerprints, diverse fingerprint poses, and significant background noise. In this paper, we propose a three-dimensional representation called Fixed-length Dense Descriptor (FDD) for efficient fingerprint matching. FDD features great spatial properties, enabling it to capture the spatial relationships of the original fingerprints, thereby enhancing interpretability and robustness. Our experiments on various fingerprint datasets reveal that FDD outperforms other fixed-length descriptors, especially in matching fingerprints of different areas, cross-modal fingerprint matching, and fingerprint matching with background noise.