# Fixed-length Dense Descriptor for Efficient Fingerprint Matching

arXiv: https://arxiv.org/abs/2311.18576 · HTML: https://mlsanity.com/paper/2311.18576/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 30 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning a Fixed-Length Fingerprint Representation](https://mlsanity.com/paper/1909.09901/) (2019)
- [FingerNet: An Unified Deep Network for Fingerprint Minutiae Extraction](https://mlsanity.com/paper/1709.02228/) (2017)
- [AFR-Net: Attention-Driven Fingerprint Recognition Network](https://mlsanity.com/paper/2211.13897/) (2022)
- [Automated Latent Fingerprint Recognition](https://mlsanity.com/paper/1704.01925/) (2017)
- [Latent Fingerprint Recognition: Fusion of Local and Global Embeddings](https://mlsanity.com/paper/2304.13800/) (2023)
- [Monocular 3D Fingerprint Reconstruction and Unwarping](https://mlsanity.com/paper/2205.00967/) (2022)
- [Secure Biometrics: Concepts, Authentication Architectures and Challenges](https://mlsanity.com/paper/1305.4832/) (2013)

## Built on by (most cited first, 7 of 7)

- [Joint Identity Verification and Pose Alignment for Partial Fingerprints](https://mlsanity.com/paper/2405.03959/) (2024)
- [Finger Pose Estimation for Under-screen Fingerprint Sensor](https://mlsanity.com/paper/2505.02481/) (2025)
- [Fixed-Length Dense Fingerprint Representation with Alignment and Robust Enhancement](https://mlsanity.com/paper/2505.03597/) (2025)
- [Minutiae-Anchored Local Dense Representation for Fingerprint Matching](https://mlsanity.com/paper/2507.15297/) (2025)
- [Identity-Consistent Multi-Pose Generation of Contactless Fingerprints](https://mlsanity.com/paper/2605.03830/) (2026)
- [Cross-Modal Registration Between 3D and 2D Fingerprints via Pose-Aware Unwrapping and Point-Cloud Fusion](https://mlsanity.com/paper/2605.15796/) (2026)
- [AFID: A Unified Open Framework for Automated Fingermark Identification, Quality Assessment and Feature Extraction](https://mlsanity.com/paper/2609.07439/) (2026)

## Abstract (arXiv)

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.