Banknote recognition by means of optimized masks, neural networks and genetic algorithms

Takeda, Fumiaki; Nishikage, Toshihiro; Omatu, Sigeru

doi:10.1016/s0952-1976(98)00061-x

Cited by 19 publications

(11 citation statements)

References 6 publications

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“…Neural network and genetic algorithms have been exploited in [11, 12] to address the problem of banknote recognition. The paper focuses mainly on how to optimize the masks exploited by a neural network to perform value recognition.…”

Section: State-of-the-artmentioning

confidence: 99%

Forgery Detection and Value Identification of Euro Banknotes

Bruna

Farinella

Guarnera

et al. 2013

Sensors

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This paper describes both hardware and software components to detect counterfeits of Euro banknotes. The proposed system is also able to recognize the banknote values. Differently than other state-of-the-art methods, the proposed approach makes use of banknote images acquired with a near infrared camera to perform recognition and authentication. This allows one to build a system that can effectively deal with real forgeries, which are usually not detectable with visible light. The hardware does not use any mechanical parts, so the overall system is low-cost. The proposed solution is reliable for ambient light and banknote positioning. Users should simply lean the banknote to be analyzed on a flat glass, and the system detects forgery, as well as recognizes the banknote value. The effectiveness of the proposed solution has been properly tested on a dataset composed by genuine and fake Euro banknotes provided by Italy's central bank.

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Section: State-of-the-artmentioning

confidence: 99%

Forgery Detection and Value Identification of Euro Banknotes

Bruna

Farinella

Guarnera

et al. 2013

Sensors

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“…A neural network and genetic algorithms has been exploited in (Takeda et al, 1999;Takeda et al, 2003) to address the problem of banknote recognition, whereas in (Khashman and Sekeroglu, 2005) the authors proposed an Intelligent Banknote Identification System (IBIS) based on neural networks technique. The system is designed for Turkish Lira and Cyprus Pounds identification.…”

Section: State Of the Artmentioning

confidence: 99%

Counterfeit Detection and Value Recognition of Euro Banknotes

Battiato

Farinella

Bruna

et al. 2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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This paper describes both hardware and software components to detect counterfeits of Euro banknotes. The system is also able to recognize the banknote values. The proposed method makes use of images acquired with near infrared camera and works without mechanical parts. This makes the overall system low-cost. The effectiveness of the proposed solution has been properly tested on a dataset composed by genuine and fake Euro banknotes provided by Italy's central bank.

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“…Previous studies using visible-light images of banknotes can be divided into those that used whole banknote images for recognition [ 1 , 2 , 3 , 4 , 5 , 6 ] and those that used certain regions of banknote images [ 7 , 8 , 9 , 10 , 11 , 12 ]. Wu et al [ 1 ] proposed a banknote orientation recognition method that uses the average brightness of eight uniform rectangles on a banknote image as the input of the classifier using a three-layer back-propagation (BP) network.…”

Section: Introductionmentioning

confidence: 99%

Efficient Banknote Recognition Based on Selection of Discriminative Regions with One-Dimensional Visible-Light Line Sensor

Pham

Park

Kwon

et al. 2016

Sensors

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Banknote papers are automatically recognized and classified in various machines, such as vending machines, automatic teller machines (ATM), and banknote-counting machines. Previous studies on automatic classification of banknotes have been based on the optical characteristics of banknote papers. On each banknote image, there are regions more distinguishable than others in terms of banknote types, sides, and directions. However, there has been little previous research on banknote recognition that has addressed the selection of distinguishable areas. To overcome this problem, we propose a method for recognizing banknotes by selecting more discriminative regions based on similarity mapping, using images captured by a one-dimensional visible light line sensor. Experimental results with various types of banknote databases show that our proposed method outperforms previous methods.

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Banknote recognition by means of optimized masks, neural networks and genetic algorithms

Cited by 19 publications

References 6 publications

Forgery Detection and Value Identification of Euro Banknotes

Forgery Detection and Value Identification of Euro Banknotes

Counterfeit Detection and Value Recognition of Euro Banknotes

Efficient Banknote Recognition Based on Selection of Discriminative Regions with One-Dimensional Visible-Light Line Sensor

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