A Robust Algorithm for Contactless Fingerprint Enhancement and Matching
- URL: http://arxiv.org/abs/2408.09426v1
- Date: Sun, 18 Aug 2024 10:01:42 GMT
- Title: A Robust Algorithm for Contactless Fingerprint Enhancement and Matching
- Authors: Mahrukh Siddiqui, Shahzaib Iqbal, Bandar AlShammari, Bandar Alhaqbani, Tariq M. Khan, Imran Razzak,
- Abstract summary: 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.
- Score: 7.820996917431323
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Compared to contact fingerprint images, contactless fingerprint images exhibit four distinct characteristics: (1) they contain less noise; (2) they have fewer discontinuities in ridge patterns; (3) the ridge-valley pattern is less distinct; and (4) they pose an interoperability problem, as they lack the elastic deformation caused by pressing the finger against the capture device. These properties present significant challenges for the enhancement of contactless fingerprint images. In this study, we propose a novel contactless fingerprint identification solution that enhances the accuracy of minutiae detection through improved frequency estimation and a new region-quality-based minutia extraction algorithm. In addition, we introduce an efficient and highly accurate minutiae-based encoding and matching algorithm. We validate the effectiveness of our approach through extensive experimental testing. Our method achieves a minimum Equal Error Rate (EER) of 2.84\% on the PolyU contactless fingerprint dataset, demonstrating its superior performance compared to existing state-of-the-art techniques. The proposed fingerprint identification method exhibits notable precision and resilience, proving to be an effective and feasible solution for contactless fingerprint-based identification systems.
Related papers
- Fingerprint Image-Quality Estimation and its Application to
Multialgorithm Verification [56.128200319868526]
Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly.
Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of structure, blur, with the help of symmetry descriptors.
The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively.
arXiv Detail & Related papers (2022-11-24T12:17:49Z) - 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) - Pair-Relationship Modeling for Latent Fingerprint Recognition [25.435974669629374]
We propose a new scheme that can model the pair-relationship of two fingerprints directly as the similarity feature for recognition.
Experimental results on two databases show that the proposed method outperforms the state of the art.
arXiv Detail & Related papers (2022-07-02T11:31:31Z) - Monocular 3D Fingerprint Reconstruction and Unwarping [36.50244665233824]
We propose a learning based shape from texture algorithm to reconstruct a 3D finger shape from a single image and unwarp the raw image to suppress perspective distortion.
Experimental results on contactless fingerprint databases show that the proposed method has high 3D reconstruction accuracy.
arXiv Detail & Related papers (2022-05-02T15:09:05Z) - 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) - A high performance fingerprint liveness detection method based on
quality related features [66.41574316136379]
The system is tested on a highly challenging database comprising over 10,500 real and fake images.
The proposed solution proves to be robust to the multi-scenario dataset, and presents an overall rate of 90% correctly classified samples.
arXiv Detail & Related papers (2021-11-02T21:09:39Z) - Responsible Disclosure of Generative Models Using Scalable
Fingerprinting [70.81987741132451]
Deep generative models have achieved a qualitatively new level of performance.
There are concerns on how this technology can be misused to spoof sensors, generate deep fakes, and enable misinformation at scale.
Our work enables a responsible disclosure of such state-of-the-art generative models, that allows researchers and companies to fingerprint their models.
arXiv Detail & Related papers (2020-12-16T03:51:54Z) - Super-resolution Guided Pore Detection for Fingerprint Recognition [22.146795282680667]
We propose a joint learning-based approach that combines both super-resolution and pore detection networks.
Our modified single image Super-Resolution Generative Adversarial Network (SRGAN) framework helps to reliably reconstruct high-resolution fingerprint samples from low-resolution ones.
arXiv Detail & Related papers (2020-12-10T20:30:56Z) - 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)
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.