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.
| Original language | English |
|---|---|
| Pages (from-to) | 294-304 |
| Number of pages | 11 |
| Journal | Digital Signal Processing: A Review Journal |
| Volume | 78 |
| DOIs | |
| Publication status | Published - 2018 Jul 1 |
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Electrical and Electronic Engineering
Cite this
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Detection of counterfeit banknotes using multispectral images. / Baek, Sangwook; Choi, Euisun; Baek, Yoonkil; Lee, Chul Hee.
In: Digital Signal Processing: A Review Journal, Vol. 78, 01.07.2018, p. 294-304.Research output: Contribution to journal › Article
TY - JOUR
T1 - Detection of counterfeit banknotes using multispectral images
AU - Baek, Sangwook
AU - Choi, Euisun
AU - Baek, Yoonkil
AU - Lee, Chul Hee
PY - 2018/7/1
Y1 - 2018/7/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85044918160&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044918160&partnerID=8YFLogxK
U2 - 10.1016/j.dsp.2018.03.015
DO - 10.1016/j.dsp.2018.03.015
M3 - Article
AN - SCOPUS:85044918160
VL - 78
SP - 294
EP - 304
JO - Digital Signal Processing: A Review Journal
JF - Digital Signal Processing: A Review Journal
SN - 1051-2004
ER -