Fixed-length Dense Descriptor for Efficient Fingerprint Matching
This work addresses efficiency and robustness issues in fingerprint recognition systems, particularly for partial or noisy prints, but it appears incremental as it builds on existing fixed-length descriptor methods.
The paper tackles the problem of fixed-length fingerprint descriptors having lower accuracy than minutiae sets, especially with incomplete prints, diverse poses, and noise, by proposing a three-dimensional Fixed-length Dense Descriptor (FDD) that improves matching performance in these challenging scenarios.
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.