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

Terhorst, Philipp ; Boller, Andre ; Damer, Naser ; Kirchbuchner, Florian ; Kuijper, Arjan (2021):
MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence.
IEEE, 2021 IEEE International Joint Conference on Biometrics (IJCB), virtual Conference, 04.-07.08.2021, ISBN 978-1-6654-3780-6,
DOI: 10.1109/IJCB52358.2021.9484404,
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Terhorst, Philipp ; Boller, Andre ; Damer, Naser ; Kirchbuchner, Florian ; Kuijper, Arjan
Title: MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence
Language: English
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.

Publisher: IEEE
ISBN: 978-1-6654-3780-6
Uncontrolled Keywords: Biometrics, Deep learning, Machine learning, Fingerprint recognition, Quality estimation
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 2021 IEEE International Joint Conference on Biometrics (IJCB)
Event Location: virtual Conference
Event Dates: 04.-07.08.2021
Date Deposited: 03 Aug 2021 07:15
DOI: 10.1109/IJCB52358.2021.9484404
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