Open AccessLow‐quality fingerprint classification using deep neural network
Author(s) -
Tertychnyi Pavlo,
Ozcinar Cagri,
Anbarjafari Gholamreza
Publication year - 2018
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
ISSN - 2047-4946
DOI - 10.1049/iet-bmt.2018.5074
Subject(s) - minutiae , computer science , fingerprint (computing) , artificial intelligence , fingerprint recognition , robustness (evolution) , pattern recognition (psychology) , artificial neural network , deep neural networks , image quality , computer vision , data mining , image (mathematics) , biochemistry , chemistry , gene
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.