research-article

Detection of counterfeit banknotes using multispectral images

Authors:

Chulhee LeeAuthors Info & Claims

Volume 78, Issue C

Pages 294 - 304

https://doi.org/10.1016/j.dsp.2018.03.015

Published: 01 July 2018 Publication History

Abstract

In this paper, we propose counterfeit banknote detection algorithms using low resolution multispectral images. It has become increasingly difficult to detect professionally produced counterfeit banknotes, so more sophisticated features have had to be implemented in banknotes. However, sensors that are capable of reading these counter-fake features are rather expensive. On the other hand, multispectral images can be used to tackle the counterfeit banknote problem. Recently, multispectral sensors have been developed for ATM applications. We developed efficient counterfeit banknote detection algorithms and the proposed algorithms were tested using 20 different denominations of European Euro (EUR), Indian rupee (INR), and US Dollars (USD). The experimental results show that the proposed methods provided 99.8% classification accuracy for genuine banknotes and 100% detection accuracy for counterfeit banknotes.

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Cited By

Niaz FZhang JZheng YKhalid MNiaz A(2024)mm-CUR: A Novel Ubiquitous, Contact-free, and Location-aware Counterfeit Currency Detection in Bundles Using Millimeter-Wave SensorACM Transactions on Sensor Networks10.1145/369497020:6(1-26)Online publication date: 5-Sep-2024
https://dl.acm.org/doi/10.1145/3694970
Pachón CBallesteros DRenza D(2023)An efficient deep learning model using network pruning for fake banknote recognitionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120961233:COnline publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120961
Baek SLee SChoi EBaek YLee C(2019)Banknote simulator for aging and soiling banknotes using Gaussian models and Perlin noiseExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.07.013137:C(405-419)Online publication date: 15-Dec-2019
https://dl.acm.org/doi/10.1016/j.eswa.2019.07.013

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Detection of counterfeit banknotes using multispectral images

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Published In

cover image Digital Signal Processing

Digital Signal Processing Volume 78, Issue C

Jul 2018

404 pages

ISSN:1051-2004

Issue’s Table of Contents

Elsevier Inc.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 July 2018

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Cited By

Niaz FZhang JZheng YKhalid MNiaz A(2024)mm-CUR: A Novel Ubiquitous, Contact-free, and Location-aware Counterfeit Currency Detection in Bundles Using Millimeter-Wave SensorACM Transactions on Sensor Networks10.1145/369497020:6(1-26)Online publication date: 5-Sep-2024
https://dl.acm.org/doi/10.1145/3694970
Pachón CBallesteros DRenza D(2023)An efficient deep learning model using network pruning for fake banknote recognitionExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120961233:COnline publication date: 15-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120961
Baek SLee SChoi EBaek YLee C(2019)Banknote simulator for aging and soiling banknotes using Gaussian models and Perlin noiseExpert Systems with Applications: An International Journal10.1016/j.eswa.2019.07.013137:C(405-419)Online publication date: 15-Dec-2019
https://dl.acm.org/doi/10.1016/j.eswa.2019.07.013

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