A Study on the Korean Banknote Recognition Using RGB and UV Information

Chae, Seung‐Hoon; Kim, Jong Kwang; Pan, Sung Bum

doi:10.1007/978-3-642-10844-0_55

Cited by 21 publications

(13 citation statements)

References 6 publications

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“…They are very useful devices to make people free from difficulties in counting banknotes, changing money or vending tickets. While sensors and scanning machines can provide visual information and has demonstrated much promise in banknote recognition, their recognitions [4, 9, 11, 14, 19-24] are restricted to specific and standard environment. An automatic system that can assist people with severe vision impairment to independently recognize banknotes is supposed to do the recognition in a wide variety of environments, such as occlusions, cluttered background, changing illumination, and different viewpoints.…”

Section: Introductionmentioning

confidence: 99%

Robust and effective component-based banknote recognition by SURF features

Hasanuzzaman

Yang

Tian

2011

2011 20th Annual Wireless and Optical Communications Conference (WOCC)

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Camera-based computer vision technology is able to assist visually impaired people to automatically recognize banknotes. A good banknote recognition algorithm for blind or visually impaired people should have the following features: 1) 100% accuracy, and 2) robustness to various conditions in different environments and occlusions. Most existing algorithms of banknote recognition are limited to work for restricted conditions. In this paper we propose a component-based framework for banknote recognition by using Speeded Up Robust Features (SURF). The component-based framework is effective in collecting more class-specific information and robust in dealing with partial occlusion and viewpoint changes. Furthermore, the evaluation of SURF demonstrates its effectiveness in handling background noise, image rotation, scale, and illumination changes. To authenticate the robustness and generalizability of the proposed approach, we have collected a large dataset of banknotes from a variety of conditions including occlusion, cluttered background, rotation, and changes of illumination, scaling, and viewpoints. The proposed algorithm achieves 100% recognition rate on our challenging dataset.

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Section: Introductionmentioning

confidence: 99%

Robust and effective component-based banknote recognition by SURF features

Hasanuzzaman

Yang

Tian

2011

2011 20th Annual Wireless and Optical Communications Conference (WOCC)

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“…Previous works on fake banknote detection used combinations of various sensor types or single sensors for capturing the optical characteristics of the banknotes analyzed. Chae et al [2] used RGB color and UV information to compare reference and input banknotes for counterfeit detection. Bake et al proposed a counterfeit banknote detection algorithm based on low-resolution multispectral images with RGB images of both sides and IR images at three frequencies.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2020

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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. INDEX TERMS Convolutional neural network, fake banknote recognition, smartphone camera, visible-light image.

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“…Fluorescent ink, invisible under normal conditions, viewable only under UV light, serves the second layer of security features. Tests confirm fluorescence only covers specific areas of the banknotes, other features such as microprinting and watermarks are difficult for machines to detect [13,14,15].…”

Section: A Featuresmentioning

confidence: 99%

Currency security and forensics: a survey

Chambers

Yan

Garhwal

et al. 2014

Multimed Tools Appl

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Abstract-By its definition, the word 'currency' refers to an agreed medium for exchange, a nation's currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desired.

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A Study on the Korean Banknote Recognition Using RGB and UV Information

Cited by 21 publications

References 6 publications

Robust and effective component-based banknote recognition by SURF features

Robust and effective component-based banknote recognition by SURF features

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

Currency security and forensics: a survey

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