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Low-quality fingerprint classification using deep neural network
journal contribution
posted on 2019-06-10, 10:09 authored by Pavlo Tertychnyi, Cagri Ozcinar, Gholamreza AnbarjafariFingerprint recognition systems mainly use minutiae points information. As shown in many previous research works,
fingerprint images do not always have good quality to be used by automatic fingerprint recognition systems. To tackle this
challenge, in this work, the authors are focusing on very low-quality fingerprint images, which contain several well-known
distortions such as dryness, wetness, physical damage, presence of dots, and blurriness. They develop an efficient, with high
accuracy, deep neural network algorithm, which recognises such low-quality fingerprints. The experimental results have been
obtained from the real low-quality fingerprint database, and the achieved results show the high performance and robustness of
the introduced deep network technique. The VGG16-based deep network achieves the highest performance of 93% for dry and
the lowest performance of 84% for blurred fingerprint classes.
Funding
This work was partially supported by Estonian Research Council Grant PUT638, the Scientific and Technological Research Council of Turkey (TÜBITAK) 1001 Project (116E097), the COST Action IC1307 iV&L Net (European Network on Integrating Vision and Language) supported by COST (European Cooperation in Science and Technology), and the Estonian Centre of Excellence in IT (EXCITE) funded by the European Regional Development Fund
History
School
- Loughborough University London
Published in
IET BiometricsVolume
7Pages
550 - 556Citation
TERTYCHNYI, P., OZCINAR, C. and ANBARJAFARI, G., 2018. Low-quality fingerprint classification using deep neural network. IET Biometrics, 7(6), pp. 550 - 556.Publisher
© The Institution of Engineering and TechnologyVersion
- VoR (Version of Record)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2018Notes
This paper is in closed access.ISSN
2047-4938eISSN
2047-4946Publisher version
Language
- en
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