Detection of counterfeit banknotes using multispectral images

Sangwook Baek, Euisun Choi, Yoonkil Baek, Chulhee Lee

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

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 languageEnglish
Pages (from-to)294-304
Number of pages11
JournalDigital Signal Processing: A Review Journal
Volume78
DOIs
Publication statusPublished - 2018 Jul

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Statistics, Probability and Uncertainty
  • Computational Theory and Mathematics
  • Electrical and Electronic Engineering
  • Artificial Intelligence
  • Applied Mathematics

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