Fixed-length Dense Descriptor for Efficient Fingerprint Matching

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.

I. Introduction

Fingerprint recognition has become a prevalent biometric technology used in various applications, such as access control, national ID cards, and law enforcement [1]. Typically, a fingerprint recognition system comprises three core modules: image acquisition, feature extraction, and matching. The feature extraction algorithm is designed to extract distinctive features, known as descriptors, from fingerprints. The matching algorithm then uses these descriptors to compute a matching score between samples. Numerous researchers have focused on developing automated and efficient algorithms for descriptor extraction and matching [2]–[5], aiming to meet the diverse demands of various fingerprint recognition applications.