Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning

• URL: http://arxiv.org/abs/2402.10847v1
• Date: Fri, 16 Feb 2024 17:36:56 GMT
• Title: Enhancement-Driven Pretraining for Robust Fingerprint Representation Learning
• Authors: Ekta Gavas, Kaustubh Olpadkar, Anoop Namboodiri
• Abstract summary: We propose a unique method for deriving robust fingerprint representations by leveraging enhancement-based pre-training. Our experimental results, tested on publicly available fingerprint datasets, reveal a marked improvement in verification performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fingerprint recognition stands as a pivotal component of biometric technology, with diverse applications from identity verification to advanced search tools. In this paper, we propose a unique method for deriving robust fingerprint representations by leveraging enhancement-based pre-training. Building on the achievements of U-Net-based fingerprint enhancement, our method employs a specialized encoder to derive representations from fingerprint images in a self-supervised manner. We further refine these representations, aiming to enhance the verification capabilities. Our experimental results, tested on publicly available fingerprint datasets, reveal a marked improvement in verification performance against established self-supervised training techniques. Our findings not only highlight the effectiveness of our method but also pave the way for potential advancements. Crucially, our research indicates that it is feasible to extract meaningful fingerprint representations from degraded images without relying on enhanced samples.

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)
• Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition [1.534667887016089]
  We propose a fusion based local matching approach towards latent fingerprint recognition. Proposed approach would integrate these handcrafted features with a recently proposed deep neural network embedding features in a multi-stage fusion approach.
  arXiv  Detail & Related papers  (2024-03-24T14:29:41Z)
• Finger-UNet: A U-Net based Multi-Task Architecture for Deep Fingerprint Enhancement [0.0]
  fingerprint enhancement plays a vital role in the early stages of the fingerprint recognition/verification pipeline. We suggest intuitive modifications to U-Net to enhance low-quality fingerprints effectively. We replace regular convolutions with depthwise separable convolutions, which significantly reduces the memory footprint of the model.
  arXiv  Detail & Related papers  (2023-10-01T09:49:10Z)
• RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images [45.73061833269094]
  We propose the first deep forgery detection approach for fingerprint images, which combines unique ridge features of fingerprint and generation artifacts of the GAN-generated images. Our proposed approach is effective and robust with low complexities.
  arXiv  Detail & Related papers  (2023-08-18T04:05:18Z)
• A Universal Latent Fingerprint Enhancer Using Transformers [47.87570819350573]
  This study aims to develop a fast method, which we call ULPrint, to enhance various latent fingerprint types. In closed-set identification accuracy experiments, the enhanced image was able to improve the performance of the MSU-AFIS from 61.56% to 75.19%.
  arXiv  Detail & Related papers  (2023-05-31T23:01:11Z)
• General GAN-generated image detection by data augmentation in fingerprint domain [3.456122013525227]
  We first separate the fingerprints and contents of the GAN-generated images using an autoencoder based GAN fingerprint extractor. The original fingerprints are substituted with the perturbed fingerprints and added to the original contents, to produce images that are visually invariant but with distinct fingerprints. To our knowledge, we are the first to conduct data augmentation in the fingerprint domain.
  arXiv  Detail & Related papers  (2022-12-27T12:37:11Z)
• 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)
• 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)
• 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)

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.