  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.
