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Low-quality fingerprint classification using deep neural network

journal contribution
posted on 10.06.2019, 10:09 by Pavlo Tertychnyi, Cagri Ozcinar, Gholamreza Anbarjafari
Fingerprint 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.
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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 Biometrics

Volume

7

Pages

550 - 556

Citation

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 Technology

Version

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

2018

Notes

This paper is in closed access.

ISSN

2047-4938

eISSN

2047-4946

Language

en

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