# Fixed-Length Dense Fingerprint Representation with Alignment and Robust Enhancement

arXiv: https://arxiv.org/abs/2505.03597 · HTML: https://mlsanity.com/paper/2505.03597/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 57 references that are in the map.
- First posted on arXiv 6 May 2025.

## Builds on (most cited first, 20 of 20)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [CosFace: Large Margin Cosine Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.09414/) (2018)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform](https://mlsanity.com/paper/1804.02815/) (2018)
- [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)
- [IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer](https://mlsanity.com/paper/2404.08237/) (2024)
- [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)
- [FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement](https://mlsanity.com/paper/2206.12885/) (2022)
- [Fixed-length Dense Descriptor for Efficient Fingerprint Matching](https://mlsanity.com/paper/2311.18576/) (2023)
- [Joint Identity Verification and Pose Alignment for Partial Fingerprints](https://mlsanity.com/paper/2405.03959/) (2024)
- [Latent Fingerprint Matching via Dense Minutia Descriptor](https://mlsanity.com/paper/2405.01199/) (2024)
- [Finger Pose Estimation for Under-screen Fingerprint Sensor](https://mlsanity.com/paper/2505.02481/) (2025)

## Built on by (most cited first, 5 of 5)

- [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)
- [UoU: A Universal Fingerprint Foundation Model Based on Large-Scale Unsupervised Learning](https://mlsanity.com/paper/2606.17436/) (2026)
- [AFID: A Unified Open Framework for Automated Fingermark Identification, Quality Assessment and Feature Extraction](https://mlsanity.com/paper/2609.07439/) (2026)

## Abstract (arXiv)

Fixed-length fingerprint representations, which map each fingerprint to a compact and fixed-size feature vector, are computationally efficient and well-suited for large-scale matching. However, designing a robust representation that effectively handles diverse fingerprint modalities, pose variations, and noise interference remains a significant challenge. In this work, we propose a fixed-length dense descriptor of fingerprints, and introduce FLARE-a fingerprint matching framework that integrates the Fixed-Length dense descriptor with pose-based Alignment and Robust Enhancement. This fixed-length representation employs a three-dimensional dense descriptor to effectively capture spatial relationships among fingerprint ridge structures, enabling robust and locally discriminative representations. To ensure consistency within this dense feature space, FLARE incorporates pose-based alignment using complementary estimation methods, along with dual enhancement strategies that refine ridge clarity while preserving the original fingerprint modality. The proposed dense descriptor supports fixed-length representation while maintaining spatial correspondence, enabling fast and accurate similarity computation. Extensive experiments demonstrate that FLARE achieves superior performance across rolled, plain, latent, and contactless fingerprints, significantly outperforming existing methods in cross-modality and low-quality scenarios. Further analysis validates the effectiveness of the dense descriptor design, as well as the impact of alignment and enhancement modules on the accuracy of dense descriptor matching. Experimental results highlight the effectiveness and generalizability of FLARE as a unified and scalable solution for robust fingerprint representation and matching. The implementation and code will be publicly available at https://github.com/Yu-Yy/FLARE.