An empirical approach for currency identification

Yan, Wei Qi; Chambers, Jarrett; Garhwal, Abhimanyu Singh

doi:10.1007/s11042-013-1833-x

Cited by 16 publications

(13 citation statements)

References 11 publications

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“…However, banknotes are still very much in circulation, and this motivated the proliferation of counterfeit banknotes that leads to susceptibility and loss of profit among traders and banks. There is a higher value attached to banknotes than coins, thus increasing the susceptible to counterfeiting, and a higher economical risk [1,2]. The counterfeit banknotes are built with security features that make it difficult to be detected.…”

Section: Introductionmentioning

confidence: 99%

The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

Khairy¹,

Hussein²,

Alrikabi³

2021

IJIES

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The movement of cash flow transactions by either electronic channels or physically created openings for the influx of counterfeit banknotes in financial markets. Aided by global economic integration and expanding international trade, attention must be geared at robust techniques for the recognition and detection of counterfeit banknotes. This paper presents ensemble learning algorithms for banknotes detection. The AdaBoost and voting ensemble are deployed in combination with machine learning algorithms. Improved detection accuracies are produced by the ensemble methods. Simulation results certify that the ensemble models of AdaBoost and voting provided accuracies of up to 100% for counterfeit banknotes.

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Section: Introductionmentioning

confidence: 99%

The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

Khairy¹,

Hussein²,

Alrikabi³

2021

IJIES

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“…Yan et.al. [20] designed a prototype for automated currency detection which is based on generally color feature and texture feature and also proposed the Feed Forward Network (FNN). Also, it measured the similarity between a real and fake banknote.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Technique for Detection of Fake Currency

Snehlata¹,

Saxena²

2019

IJRTE

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During the past years, some of the researchers are using the matching techniques for identification of the fake currency either by using the Mathematical formulation or by using the readymade simulation tools. A lot of methods namely edge detection, segmentation, feature extraction, pattern matching has been used for finding and identification of the fake currency. In the present work, Principal Component Analysis (PCA) is used to detect the feature of currency through modeling and a proposed algorithm is elaborated to recognize the fake currency in the form of note Rs 2000 of Indian currency. Graphs are also designed to justify the present approach along with the comparison of results

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“…There are also methods using RGB, HSV, or features in the HSI color space [37,40,45,49,50,61,68], methods using edge-based features expressed with Canny, Prewitt, or Sobel operators [40,44,54,60], and methods using histogram information-based features such as correlation, central moments, kurtosis, mean, standard deviation, and skewness [39,43,53,59,64,65]. …”

Section: Banknote Recognitionmentioning

confidence: 99%

A Survey on Banknote Recognition Methods by Various Sensors

Lee

Hong

Kim

et al. 2017

Sensors

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Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them.

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An empirical approach for currency identification

Cited by 16 publications

References 11 publications

The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

The Detection of Counterfeit Banknotes Using Ensemble Learning Techniques of AdaBoost and Voting

An Efficient Technique for Detection of Fake Currency

A Survey on Banknote Recognition Methods by Various Sensors

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