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.

翻译：在指纹匹配中，固定长度描述符通常比细节点集具有更高的效率，但其识别精度不及后者。尽管基于深度学习的固定长度描述符近期已取得显著进展，但在处理不完整或部分指纹、多样化的指纹姿态以及显著背景噪声时，它们往往表现不足。本文提出一种称为固定长度密集描述符的三维表示方法，用于高效指纹匹配。FDD具备优越的空间特性，使其能够捕捉原始指纹的空间关系，从而增强可解释性与鲁棒性。我们在多个指纹数据集上的实验表明，FDD优于其他固定长度描述符，尤其在匹配不同区域的指纹、跨模态指纹匹配以及存在背景噪声的指纹匹配任务中表现突出。