Deep Learning-Based Fake-Banknote Detection for the Visually Impaired People Using Visible-Light Images Captured by Smartphone Cameras

Tuyen Danh Pham, Chanhum Park, Dat Tien Nguyen, Ganbayar Batchuluun, Kang Ryoung Park

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Automatic recognition of fake banknotes is an important task in practical banknote handling. Research on this task has mostly involved methods applied to automatic sorting machines with multiple imaging sensors or that use specialized sensors for capturing banknote images in various light wavelengths. These approaches can make use of the security features on banknotes for counterfeit detection. However, they require specialized devices, which are not always available for general users or visually impaired people. Meanwhile, smartphones are becoming more popular and can be useful imaging devices. Moreover, the types of fake banknotes created by imaging devices such as smartphone cameras or scanners are sometimes cannot be recognized by especially the visually impaired people. Addressing these problems, we propose a method for classifying fake and genuine banknotes using visible-light images captured by smartphone cameras based on convolutional neural networks. Experimental results on a self-collected dataset of US dollar, Euro, Korean won, and Jordanian dinar banknotes showed that our method performs better in terms of fake detection than the state-of-the-art methods.

Original languageEnglish
Article number9050503
Pages (from-to)63144-63161
Number of pages18
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • Convolutional neural network
  • fake banknote recognition
  • smartphone camera
  • visible-light image

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