Super-resolution Guided Pore Detection for Fingerprint Recognition

• URL: http://arxiv.org/abs/2012.05959v1
• Date: Thu, 10 Dec 2020 20:30:56 GMT
• Title: Super-resolution Guided Pore Detection for Fingerprint Recognition
• Authors: Syeda Nyma Ferdous, Ali Dabouei, Jeremy Dawson, Nasser M Nasrabadi
• Abstract summary: 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.
• Score: 22.146795282680667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Performance of fingerprint recognition algorithms substantially rely on fine features extracted from fingerprints. Apart from minutiae and ridge patterns, pore features have proven to be usable for fingerprint recognition. Although features from minutiae and ridge patterns are quite attainable from low-resolution images, using pore features is practical only if the fingerprint image is of high resolution which necessitates a model that enhances the image quality of the conventional 500 ppi legacy fingerprints preserving the fine details. To find a solution for recovering pore information from low-resolution fingerprints, we adopt 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 assisting the pore detection network to identify pores with a high accuracy. The network jointly learns a distinctive feature representation from a real low-resolution fingerprint sample and successfully synthesizes a high-resolution sample from it. To add discriminative information and uniqueness for all the subjects, we have integrated features extracted from a deep fingerprint verifier with the SRGAN quality discriminator. We also add ridge reconstruction loss, utilizing ridge patterns to make the best use of extracted features. Our proposed method solves the recognition problem by improving the quality of fingerprint images. High recognition accuracy of the synthesized samples that is close to the accuracy achieved using the original high-resolution images validate the effectiveness of our proposed model.

Related papers

• 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)
• DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection [67.3143177137102]
  Deepfake detection refers to detecting artificially generated or edited faces in images or videos. We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
  arXiv  Detail & Related papers  (2023-12-07T07:19:45Z)
• 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)
• Cross-resolution Face Recognition via Identity-Preserving Network and Knowledge Distillation [12.090322373964124]
  Cross-resolution face recognition is a challenging problem for modern deep face recognition systems. This paper proposes a new approach that enforces the network to focus on the discriminative information stored in the low-frequency components of a low-resolution image.
  arXiv  Detail & Related papers  (2023-03-15T14:52:46Z)
• 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)
• On the Effects of Image Quality Degradation on Minutiae- and Ridge-Based Automatic Fingerprint Recognition [61.81926091202142]
  We study the performance of two fingerprint matchers based on minutiae and ridge information under varying image quality. The ridge-based system is found to be more robust to image quality degradation than the minutiae-based system for a number of different image quality criteria.
  arXiv  Detail & Related papers  (2022-07-12T10:28:36Z)
• Fingerprint Liveness Detection Based on Quality Measures [66.76480178498315]
  The novel feature set is used in a complete liveness detection system and tested on the development set of the LivDET competition. The proposed solution proves to be robust to the multi-sensor scenario, and presents an overall rate of 93% of correctly classified samples.
  arXiv  Detail & Related papers  (2022-07-11T12:15:27Z)
• 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)
• Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition [7.6702700993064115]
  We propose an identity-preserving end-to-end image-to-image translation deep neural network. It is capable of super-resolving very low-resolution faces to their high-resolution counterparts while preserving identity-related information.
  arXiv  Detail & Related papers  (2020-10-23T09:21:06Z)

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.