Direct Regression of Distortion Field from a Single Fingerprint Image

• URL: http://arxiv.org/abs/2404.17148v1
• Date: Fri, 26 Apr 2024 04:35:42 GMT
• Title: Direct Regression of Distortion Field from a Single Fingerprint Image
• Authors: Xiongjun Guan, Yongjie Duan, Jianjiang Feng, Jie Zhou,
• Abstract summary: Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. Existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose.
• Score: 32.77128793787146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint and then rectifying it into a normal fingerprint. However, existing rectification methods are based on principal component representation of distortion fields, which is not accurate and are very sensitive to finger pose. In this paper, we propose a rectification method where a self-reference based network is utilized to directly estimate the dense distortion field of distorted fingerprint instead of its low dimensional representation. This method can output accurate distortion fields of distorted fingerprints with various finger poses. Considering the limited number and variety of distorted fingerprints in the existing public dataset, we collected more distorted fingerprints with diverse finger poses and distortion patterns as a new database. Experimental results demonstrate that our proposed method achieves the state-of-the-art rectification performance in terms of distortion field estimation and rectified fingerprint matching.

Related papers

• 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)
• Regression of Dense Distortion Field from a Single Fingerprint Image [32.77128793787146]
  Skin distortion is a long standing challenge in fingerprint matching, which causes false non-matches. Previous studies have shown that the recognition rate can be improved by estimating the distortion field from a distorted fingerprint. We propose a rectification method where a self-reference based network is utilized to directly estimate the dense distortion field of distorted fingerprint.
  arXiv  Detail & Related papers  (2024-04-26T05:00:51Z)
• 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)
• 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)
• ProxyFAUG: Proximity-based Fingerprint Augmentation [81.15016852963676]
  ProxyFAUG is a rule-based, proximity-based method of fingerprint augmentation. The best performing positioning method on this dataset is improved by 40% in terms of median error and 6% in terms of mean error, with the use of the augmented dataset.
  arXiv  Detail & Related papers  (2021-02-04T15:59:30Z)
• 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)
• A Deep Ordinal Distortion Estimation Approach for Distortion Rectification [62.72089758481803]
  We propose a novel distortion rectification approach that can obtain more accurate parameters with higher efficiency. We design a local-global associated estimation network that learns the ordinal distortion to approximate the realistic distortion distribution. Considering the redundancy of distortion information, our approach only uses a part of distorted image for the ordinal distortion estimation.
  arXiv  Detail & Related papers  (2020-07-21T10:03:42Z)
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data [64.65952078807086]
  Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs) Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. We seek a proactive and sustainable solution on deepfake detection by introducing artificial fingerprints into the models.
  arXiv  Detail & Related papers  (2020-07-16T16:49:55Z)
• 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.