Terhörst, Philipp ; Boller, Andre ; Damer, Naser ; Kirchbuchner, Florian ; Kuijper, Arjan (2021)
MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence.
2021 IEEE International Joint Conference on Biometrics (IJCB). virtual Conference (04.-07.08.2021)
doi: 10.1109/IJCB52358.2021.9484404
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Terhörst, Philipp ; Boller, Andre ; Damer, Naser ; Kirchbuchner, Florian ; Kuijper, Arjan |
| Art des Eintrags: | Bibliographie |
| Titel: | MiDeCon: Unsupervised and Accurate Fingerprint and Minutia Quality Assessment based on Minutia Detection Confidence |
| Sprache: | Englisch |
| Publikationsjahr: | 20 Juli 2021 |
| Verlag: | IEEE |
| Veranstaltungstitel: | 2021 IEEE International Joint Conference on Biometrics (IJCB) |
| Veranstaltungsort: | virtual Conference |
| Veranstaltungsdatum: | 04.-07.08.2021 |
| DOI: | 10.1109/IJCB52358.2021.9484404 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Biometrics, Deep learning, Machine learning, Fingerprint recognition, Quality estimation |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme 20 Fachbereich Informatik > Mathematisches und angewandtes Visual Computing |
| Hinterlegungsdatum: | 03 Aug 2021 07:15 |
| Letzte Änderung: | 27 Feb 2023 11:25 |
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