A Single-step Accurate Fingerprint Registration Method Based on Local Feature Matching
- URL: http://arxiv.org/abs/2507.16201v1
- Date: Tue, 22 Jul 2025 03:29:46 GMT
- Title: A Single-step Accurate Fingerprint Registration Method Based on Local Feature Matching
- Authors: Yuwei Jia, Zhe Cui, Fei Su,
- Abstract summary: Distortion of the fingerprint images leads to a decline in fingerprint recognition performance.<n>We propose an end-to-end single-step fingerprint registration algorithm that aligns two fingerprints.<n>Experiment results prove that our method can achieve the state-of-the-art matching performance with only single-step registration.
- Score: 13.210407694270362
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Distortion of the fingerprint images leads to a decline in fingerprint recognition performance, and fingerprint registration can mitigate this distortion issue by accurately aligning two fingerprint images. Currently, fingerprint registration methods often consist of two steps: an initial registration based on minutiae, and a dense registration based on matching points. However, when the quality of fingerprint image is low, the number of detected minutiae is reduced, leading to frequent failures in the initial registration, which ultimately causes the entire fingerprint registration process to fail. In this study, we propose an end-to-end single-step fingerprint registration algorithm that aligns two fingerprints by directly predicting the semi-dense matching points correspondences between two fingerprints. Thus, our method minimizes the risk of minutiae registration failure and also leverages global-local attentions to achieve end-to-end pixel-level alignment between the two fingerprints. Experiment results prove that our method can achieve the state-of-the-art matching performance with only single-step registration, and it can also be used in conjunction with dense registration algorithms for further performance improvements.
Related papers
- Scalable Fingerprinting of Large Language Models [46.26999419117367]
We introduce a new method, dubbed Perinucleus sampling, to generate scalable, persistent, and harmless fingerprints.<n>We demonstrate that this scheme can add 24,576 fingerprints to a Llama-3.1-8B model without degrading the model's utility.
arXiv Detail & Related papers (2025-02-11T18:43:07Z) - Latent fingerprint enhancement for accurate minutiae detection [8.996826918574463]
We propose a novel approach that uses generative adversary networks (GANs) to redefine Latent Fingerprint Enhancement (LFE)
By directly optimising the minutiae information during the generation process, the model produces enhanced latent fingerprints that exhibit exceptional fidelity to ground-truth instances.
Our framework integrates minutiae locations and orientation fields, ensuring the preservation of both local and structural fingerprint features.
arXiv Detail & Related papers (2024-09-18T08:35:31Z) - A Robust Algorithm for Contactless Fingerprint Enhancement and Matching [7.820996917431323]
contactless fingerprint images exhibit four distinct characteristics.
They contain less noise, have fewer discontinuities in ridge patterns, and pose an interoperability problem.
We propose a novel contactless fingerprint identification solution that enhances the accuracy of minutiae detection.
arXiv Detail & Related papers (2024-08-18T10:01:42Z) - Latent Fingerprint Recognition: Fusion of Local and Global Embeddings [30.40684369054112]
We combine global embeddings with local embeddings for state-of-the-art latent to rolled matching accuracy with high throughput.
We show the generalizability of the fused representations for improving authentication accuracy across several rolled, plain, and contactless fingerprint datasets.
arXiv Detail & Related papers (2023-04-26T19:42:57Z) - FIGO: Enhanced Fingerprint Identification Approach Using GAN and One
Shot Learning Techniques [0.0]
We propose a Fingerprint Identification approach based on Generative adversarial network and One-shot learning techniques.
First, we propose a Pix2Pix model to transform low-quality fingerprint images to a higher level of fingerprint images pixel by pixel directly in the fingerprint enhancement tier.
Second, we construct a fully automated fingerprint feature extraction model using a one-shot learning approach to differentiate each fingerprint from the others in the fingerprint identification process.
arXiv Detail & Related papers (2022-08-11T02:45:42Z) - A review of schemes for fingerprint image quality computation [66.32254395574994]
This paper reviews existing approaches for fingerprint image quality computation.
We also implement, test and compare a selection of them using the MCYT database including 9000 fingerprint images.
arXiv Detail & Related papers (2022-07-12T10:34:03Z) - On the vulnerability of fingerprint verification systems to fake
fingerprint attacks [57.36125468024803]
A medium-size fake fingerprint database is described and two different fingerprint verification systems are evaluated on it.
Results for an optical and a thermal sweeping sensors are given.
arXiv Detail & Related papers (2022-07-11T12:22:52Z) - SpoofGAN: Synthetic Fingerprint Spoof Images [47.87570819350573]
A major limitation to advances in fingerprint spoof detection is the lack of publicly available, large-scale fingerprint spoof datasets.
This work aims to demonstrate the utility of synthetic (both live and spoof) fingerprints in supplying these algorithms with sufficient data.
arXiv Detail & Related papers (2022-04-13T16:27:27Z) - A Comparative Study of Fingerprint Image-Quality Estimation Methods [54.84936551037727]
Poor-quality images result in spurious and missing features, thus degrading the performance of the overall system.
In this work, we review existing approaches for fingerprint image-quality estimation.
We have also tested a selection of fingerprint image-quality estimation algorithms.
arXiv Detail & Related papers (2021-11-14T19:53:12Z) - Latent Fingerprint Registration via Matching Densely Sampled Points [100.53031290339483]
Existing latent fingerprint registration approaches are mainly based on establishing correspondences between minutiae.
We propose a non-minutia latent fingerprint registration method which estimates the spatial transformation between a pair of fingerprints.
The proposed method achieves the state-of-the-art registration performance, especially under challenging conditions.
arXiv Detail & Related papers (2020-05-12T15:51:59Z) - Dense Registration and Mosaicking of Fingerprints by Training an
End-to-End Network [36.50244665233824]
We train an end-to-end network to output pixel-wise displacement field between two fingerprints.
We also propose a fingerprint mosaicking method based on optimal seam selection.
Our registration method outperforms previous dense registration methods in accuracy and efficiency.
arXiv Detail & Related papers (2020-04-13T14:47:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.