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  4. MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence
 
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2021
Conference Paper
Title

MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence

Abstract
An essential factor to achieve high accuracies in finger-print recognition systems is the quality of its samples. Previous works mainly proposed supervised solutions based on image properties that neglects the minutiae extraction process, despite that most fingerprint recognition techniques are based on detected minutiae. Consequently, a fingerprint image might be assigned a high quality even if the utilized minutia extractor produces unreliable information. In this work, 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. We propose using the detection reliability of the extracted minutia as its quality indicator. By combining the highest minutia qualities, MiDeCon also accurately determines the quality of a full fingerprint. Experiments are conducted on the publicly available databases of the FVC 2006 and compared against several baselines, such as NIST's widely-used fingerprint image quality software NFIQ1 and NFIQ2. The results demonstrate a significantly stronger quality assessment performance of the proposed MiDeCon-qualities as related works on both, minutia- and fingerprint-level. The implementation is publicly available.
Author(s)
Terhörst, Philipp  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Boller, André
TU Darmstadt GRIS
Damer, Naser  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kirchbuchner, Florian  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Kuijper, Arjan  orcid-logo
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
IEEE International Joint Conference on Biometrics, IJCB 2021  
Project(s)
ATHENE
Funder
Bundesministerium für Bildung und Forschung BMBF (Deutschland)  
Conference
International Joint Conference on Biometrics (IJCB) 2021  
DOI
10.1109/IJCB52358.2021.9484404
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • Lead Topic: Digitized Work

  • Lead Topic: Smart City

  • Research Line: Computer vision (CV)

  • Research Line: Machine Learning (ML)

  • biometrics

  • deep learning

  • machine learning

  • quality estimation

  • fingerprint recognition

  • ATHENE

  • CRISP

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