UFQA: Utility guided Fingerphoto Quality Assessment

• URL: http://arxiv.org/abs/2407.11141v1
• Date: Mon, 15 Jul 2024 18:06:07 GMT
• Title: UFQA: Utility guided Fingerphoto Quality Assessment
• Authors: Amol S. Joshi, Ali Dabouei, Jeremy Dawson, Nasser Nasrabadi,
• Abstract summary: We propose a self-supervised dual encoder framework to learn meaningful feature representations to assess fingerphoto quality. A quality prediction model is trained to assess fingerphoto quality with additional supervision of quality maps. Experimental results verify that our approach performs better than the widely used fingerprint quality metric NFIQ2.2.
• Score: 5.978350039412277
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Quality assessment of fingerprints captured using digital cameras and smartphones, also called fingerphotos, is a challenging problem in biometric recognition systems. As contactless biometric modalities are gaining more attention, their reliability should also be improved. Many factors, such as illumination, image contrast, camera angle, etc., in fingerphoto acquisition introduce various types of distortion that may render the samples useless. Current quality estimation methods developed for fingerprints collected using contact-based sensors are inadequate for fingerphotos. We propose Utility guided Fingerphoto Quality Assessment (UFQA), a self-supervised dual encoder framework to learn meaningful feature representations to assess fingerphoto quality. A quality prediction model is trained to assess fingerphoto quality with additional supervision of quality maps. The quality metric is a predictor of the utility of fingerphotos in matching scenarios. Therefore, we use a holistic approach by including fingerphoto utility and local quality when labeling the training data. Experimental results verify that our approach performs better than the widely used fingerprint quality metric NFIQ2.2 and state-of-the-art image quality assessment algorithms on multiple publicly available fingerphoto datasets.

Related papers

• Helping Visually Impaired People Take Better Quality Pictures [52.03016269364854]
  We develop tools to help visually impaired users minimize occurrences of common technical distortions. We also create a prototype feedback system that helps to guide users to mitigate quality issues.
  arXiv  Detail & Related papers  (2023-05-14T04:37:53Z)
• 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)
• 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 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)
• MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence [6.284767263654553]
  We propose a novel concept of assessing minutia and fingerprint quality based on minutia detection confidence (MiDeCon) MiDeCon can be applied to an arbitrary deep learning based minutia extractor and does not require quality labels for learning. Experiments are conducted on the publicly available databases of the FVC 2006 and compared against several baselines.
  arXiv  Detail & Related papers  (2021-06-10T09:06:01Z)

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.