US20070140551A1 - Banknote validation - Google Patents

Banknote validation Download PDF

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Publication number
US20070140551A1
US20070140551A1 US11/366,147 US36614706A US2007140551A1 US 20070140551 A1 US20070140551 A1 US 20070140551A1 US 36614706 A US36614706 A US 36614706A US 2007140551 A1 US2007140551 A1 US 2007140551A1
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United States
Prior art keywords
images
classifier
banknote
information
training set
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
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US11/366,147
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English (en)
Inventor
Chao He
Gary Ross
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NCR Voyix Corp
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Individual
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Publication date
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First worldwide family litigation filed litigation Critical https://patents.darts-ip.com/?family=37529297&utm_source=google_patent&utm_medium=platform_link&utm_campaign=public_patent_search&patent=US20070140551(A1) "Global patent litigation dataset” by Darts-ip is licensed under a Creative Commons Attribution 4.0 International License.
Application filed by Individual filed Critical Individual
Priority to US11/366,147 priority Critical patent/US20070140551A1/en
Assigned to NCR CORPORATION reassignment NCR CORPORATION ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: HE, CHAO, ROSS, GARY
Priority to JP2008545069A priority patent/JP5219211B2/ja
Priority to BRPI0619845-7A priority patent/BRPI0619845A2/pt
Priority to EP06779545A priority patent/EP1964073A1/en
Priority to CN2006800473583A priority patent/CN101331526B/zh
Priority to PCT/GB2006/003565 priority patent/WO2007068867A1/en
Priority to EP06820517A priority patent/EP1964075A1/en
Priority to BRPI0620308-6A priority patent/BRPI0620308A2/pt
Priority to JP2008545085A priority patent/JP5044567B2/ja
Priority to EP06831386A priority patent/EP1964076A1/en
Priority to PCT/GB2006/004670 priority patent/WO2007068928A1/en
Priority to JP2008545086A priority patent/JP5177817B2/ja
Priority to CN2006800473687A priority patent/CN101366061B/zh
Priority to PCT/GB2006/004676 priority patent/WO2007068930A1/en
Priority to PCT/GB2006/004663 priority patent/WO2007068923A1/en
Priority to BRPI0620625-5A priority patent/BRPI0620625A2/pt
Priority to EP06820512A priority patent/EP1964074A1/en
Priority to JP2008545088A priority patent/JP5175210B2/ja
Priority to CN2006800475165A priority patent/CN101331527B/zh
Priority to CN2006800472788A priority patent/CN101366060B/zh
Priority to BRPI0619926-7A priority patent/BRPI0619926A2/pt
Priority to US11/639,576 priority patent/US8086017B2/en
Priority to US11/639,597 priority patent/US20070154079A1/en
Priority to US11/639,593 priority patent/US20070154078A1/en
Publication of US20070140551A1 publication Critical patent/US20070140551A1/en
Assigned to JPMORGAN CHASE BANK, N.A., AS ADMINISTRATIVE AGENT reassignment JPMORGAN CHASE BANK, N.A., AS ADMINISTRATIVE AGENT SECURITY AGREEMENT Assignors: NCR CORPORATION, NCR INTERNATIONAL, INC.
Assigned to NCR VOYIX CORPORATION reassignment NCR VOYIX CORPORATION RELEASE OF PATENT SECURITY INTEREST Assignors: JPMORGAN CHASE BANK, N.A., AS ADMINISTRATIVE AGENT
Abandoned legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G07—CHECKING-DEVICES
    • G07D—HANDLING OF COINS OR VALUABLE PAPERS, e.g. TESTING, SORTING BY DENOMINATIONS, COUNTING, DISPENSING, CHANGING OR DEPOSITING
    • G07D7/00—Testing specially adapted to determine the identity or genuineness of valuable papers or for segregating those which are unacceptable, e.g. banknotes that are alien to a currency
    • G07D7/20—Testing patterns thereon
    • G07D7/202—Testing patterns thereon using pattern matching
    • G07D7/206—Matching template patterns

Definitions

  • the present invention relates to a method and apparatus for banknote validation.
  • a currency validator determines whether a given banknote is genuine or counterfeit.
  • Previous automatic validation methods typically require a relatively large number of examples of counterfeit banknotes to be known in order to train the classifier.
  • those previous classifiers are trained to detect known counterfeits only. This is problematic because often little or no information is available about possible counterfeits. For example, this is particularly problematic for newly introduced denominations or newly introduced currency.
  • the invention seeks to provide an improved method and apparatus for banknote validation which overcomes or at least mitigates one or more of the problems mentioned above.
  • a method of creating a classifier for banknote validation is described.
  • Information from all of a set of training images from genuine banknotes is used to form a segmentation template which is then used to segment each of the training set images.
  • Features are extracted from the segments and used to form a classifier which is preferably a one-class statistical classifier.
  • Classifiers can be quickly and simply formed for different currencies and denominations in this way and without the need for examples of counterfeit banknotes.
  • a banknote validator using such a classifier is described as well as a method of validating a banknote using such a classifier.
  • the banknote validator is incorporated in a self-service apparatus such as an automated teller machine.
  • the information from all images in the training set comprises morphological information.
  • This can be pattern, color, texture and the like in the training set. We have found empirically that use of this type of information leads to improved banknote validation performance.
  • the information from all images in the training set comprises information about a pixel at the same location in each of the training set images.
  • This can comprises pixel intensity profiles as explained in more detail below.
  • the segmentation template is created by using a clustering algorithm to cluster pixel locations in an image plane on the basis of the information from all the images in the training set.
  • a clustering algorithm Any suitable clustering algorithm can be used as known in the art.
  • the classifier is a one-class classifier. This is advantageous because by using a one-class classifier and the method of forming the segmentation template described above, we can remove the need for examples of counterfeit banknotes in the training set. Thus, preferably the training set images are of genuine banknotes only.
  • the classifier is a statistical one-class classifier. These are typically less computationally intensive and perform better than neural network based approaches.
  • the step of forming the classifier comprises estimating a distribution of a statistic relating to banknotes in a target class, said target class comprising genuine currency.
  • the training set images are selected from any of reflection images, transmission images, visible information, non-visible information and other images such as magnetic, thermal and x-ray images.
  • the classifier can be formed on the basis of specified information about a particular denomination and currency of banknotes. For example, information about particularly data rich regions in terms of color or other information, spatial frequency or shapes in a given currency and denomination.
  • the invention also encompasses an apparatus for creating a banknote classifier comprising:
  • the invention also encompasses a banknote validator comprising:
  • the banknote validator further comprises a plurality of classifiers and a combiner arranged to combine the results of each of the classifiers.
  • the invention also encompasses a method of validating a banknote comprising:
  • the invention also encompasses a computer program comprising computer program code means adapted to perform all the steps of any of the methods described above when said program is run on a computer.
  • the computer program can be embodied on a computer readable medium.
  • the invention also encompasses a self-service apparatus comprising:
  • the method may be performed by software in machine readable form on a storage medium.
  • the method steps may be carried out in any suitable order and/or in parallel as is apparent to the skilled person in the art.
  • FIG. 1 is a flow diagram of a method of creating a classifier for banknote validation
  • FIG. 2 is a schematic diagram of an apparatus for creating a classifier for banknote validation
  • FIG. 3 is a schematic diagram of a banknote validator
  • FIG. 4 is a flow diagram of a method of validating a banknote
  • FIG. 5 is a schematic diagram of a self-service apparatus with a banknote validator.
  • Embodiments of the present invention are described below by way of example only. These examples represent the best ways of putting the invention into practice that are currently known to the Applicant although they are not the only ways in which this could be achieved.
  • one class classifier is used to refer to a classifier that is formed or built using information about examples only from a single class but which is used to allocate newly presented examples either to that single class or not. This differs from a conventional binary classifier which is created using information about examples from two classes and which is used to allocate new examples to one or other of those two classes.
  • a one-class classifier can be thought of as defining a boundary around a known class such that examples falling out with that boundary are deemed not to belong to the known class.
  • FIG. 1 is a high level flow diagram of a method of creating a classifier for banknote validation.
  • a training set of images of genuine banknotes (see box 10 of FIG. 1 ). These are images of the same type taken of banknotes of the same currency and denomination.
  • the type of image relates to how the images are obtained, and this may be in any manner known in the art. For example, reflection images, transmission images, images on any of a red, blue or green channel, thermal images, infrared images, ultraviolet images, x-ray images or other image types.
  • the images in the training set are in registration and are the same size. Pre-processing can be carried out to align the images and scale them to size if necessary, as known in the art.
  • the segmentation template comprises information about how to divide an image into a plurality of segments.
  • the segments may be non-continuous, that is, a given segment can comprise more than one patch in different regions of the image.
  • the segmentation template also comprises a specified number of segments to be used.
  • feature we mean any statistic or other characteristic of a segment. For example, the mean pixel intensity, median pixel intensity, mode of the pixel intensities, texture, histogram, Fourier transform descriptors, wavelet transform descriptors and/or any other statistics in a segment.
  • a classifier is then formed using the feature information (see box 18 of FIG. 1 ).
  • Any suitable type of classifier can be used as known in the art.
  • the classifier is a one-class classifier and no information about counterfeit banknotes is needed.
  • the method in FIG. 1 enables a classifier for validation of banknotes of a particular currency and denomination to be formed simply, quickly and effectively. To create classifiers for other currencies or denominations the method is repeated with appropriate training set images.
  • the present invention uses a different method of forming the segmentation template which removes the need for using a genetic algorithm or equivalent method to search for a good segmentation template within a large number of possible segmentation templates. This reduces computational cost and improves performance. In addition the need for information about counterfeit banknotes is removed.
  • this method can be thought of as specifying how to divide the image plane into a plurality of segments, each comprising a plurality of specified pixels.
  • the segments can be non-continuous as mentioned above.
  • this specification is made on the basis of information from all images in the training set.
  • segmentation using a rigid grid structure does not require information from images in the training set.
  • pixel intensity profiles In a preferred example we use these pixel intensity profiles. However, it is not essential to use pixel intensity profiles. It is also possible to use other information from all images in the training set. For example, intensity profiles for blocks of 4 neighboring pixels or mean values of pixel intensities for pixels at the same location in each of the training set images.
  • a row vector ⁇ a ji , a j2 , . . . , a jN ⁇ in A can be seen as an intensity profiled for a particular pixel (jth) across N images. If two pixels come from the same pattern region of the image they are likely to have the similar intensity values and hence have a strong temporal correlation. Note the term “temporal” here need not exactly correspond to the time axis but is borrowed to indicate the axis across different images in the ensemble. Our algorithm tries to find these correlations and segments the image plane spatially into regions of pixels that have similar temporal behavior. We measure this correlation by defining a metric between intensity profiles. A simple way is to use the Euclidean distance, i.e.
  • d(j,k) the stronger the correlation between the two pixels.
  • the image plane In order to decompose the image plane spatially using the temporal correlations between pixels, we run a clustering algorithm on the pixel intensity profiles (the rows of the design matrix A. It will produce clusters of temporally correlated pixels. The most straightforward choice is to employ the K-means algorithm, but it could be any other clustering algorithm. As a result the image plane is segmented into several segments of temporally correlated pixels. This can then be used as a template to segment all images in the training set; and a classifier can be built on features extracted from those segments of all images in the training set.
  • one-class classifier is preferable. Any suitable type of one-class classifier can be used as known in the art. For example, neural network based one-class classifiers and statistical based one-class classifiers.
  • Suitable statistical methods for one-class classification are in general based on maximization of the log-likelihood ratio under the null-hypothesis that the observation under consideration is drawn from the target class and these include the D 2 test (described in Morrison, DF: Multivariate Statistical Methods (third edition). McGraw-Hill Publishing Company, New York, 1990) which assumes a multivariate Gaussian distribution for the target class (genuine currency).
  • the density of the target class can be estimated using for example a semi-parametric Mixture of Gaussians (described in Bishop, CM: Neural Networks for Pattern Recognition, Oxford University Press, New York, 1995) or a non-parametric Parzen window (described in Duda, R.
  • Support Vector Data Domain Description (described in Tax, DMJ, Duin, RPW: Support vector domain description, Pattern Recognition Letters, 20(11-12). 1191 -1199, 1999), also known as ‘support estimation’ (described in Hayton, P, Schblkopf, B, Tarrassenko, L, Anuzis, P: Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra, Advances in Neural Information Processing Systems, 13, eds Leen, Todd K and Dietterich, Thomas G and Tresp, Volker, MIT Press, 946-952, 2001) and Extreme Value Theory (EVT) (described in Roberts, S.
  • SVDD Support Vector Data Domain Description
  • EDT Extreme Value Theory
  • ⁇ ) sup ⁇ ⁇ ⁇ n 1 N ⁇ ⁇ ( x n
  • semi-parametric e.g. Gaussian Mixture Model
  • non-parametric e.g. Parzen window method
  • B bootstrap replicates of the test statistic ⁇ crit i , i 1, . . . , B can be obtained by randomly selecting an N+1′th sample and computing ⁇ circumflex over (p) ⁇ (x N+1 ; ⁇ circumflex over ( ⁇ ) ⁇ N i ) ⁇ crit i .
  • the method of forming the classifier is repeated for different numbers of segments and tested using images of banknotes known to be either counterfeit or not.
  • the number of segments giving the best performance is then selected and the classifier using that number of segments used. We found that the best number of segments to be from about 2 to 12 although any suitable number of segments can be used.
  • FIG. 2 is a schematic diagram of an apparatus 20 for creating a classifier 22 for banknote validation. It comprises:
  • FIG. 3 is a schematic diagram of a banknote validator 31 . It comprises:
  • FIG. 4 is a flow diagram of a method of validating a banknote. The method comprises:
  • the explicit information in the segmentation template can be a simple file with a list of pixel addresses to be included in each segment.
  • FIG. 5 is a schematic diagram of a self-service apparatus 51 with a banknote validator 53 . It comprises:
  • the means for accepting banknotes is of any suitable type as known in the art as is the imaging means.

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  • Engineering & Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Inspection Of Paper Currency And Valuable Securities (AREA)
  • Credit Cards Or The Like (AREA)
  • Image Analysis (AREA)
US11/366,147 2005-12-16 2006-03-02 Banknote validation Abandoned US20070140551A1 (en)

Priority Applications (24)

Application Number Priority Date Filing Date Title
US11/366,147 US20070140551A1 (en) 2005-12-16 2006-03-02 Banknote validation
JP2008545069A JP5219211B2 (ja) 2005-12-16 2006-09-26 銀行券の確認方法及びその装置
BRPI0619845-7A BRPI0619845A2 (pt) 2005-12-16 2006-09-26 validação de nota bancária
EP06779545A EP1964073A1 (en) 2005-12-16 2006-09-26 Banknote validation
CN2006800473583A CN101331526B (zh) 2005-12-16 2006-09-26 纸币验证
PCT/GB2006/003565 WO2007068867A1 (en) 2005-12-16 2006-09-26 Banknote validation
CN2006800472788A CN101366060B (zh) 2005-12-16 2006-12-14 介质验证
BRPI0619926-7A BRPI0619926A2 (pt) 2005-12-16 2006-12-14 detecção de itens de mìdia falsificados de melhor qualidade
JP2008545086A JP5177817B2 (ja) 2005-12-16 2006-12-14 媒体確認方法及び媒体確認装置
JP2008545088A JP5175210B2 (ja) 2005-12-16 2006-12-14 メディアの確認装置および確認方法
JP2008545085A JP5044567B2 (ja) 2005-12-16 2006-12-14 媒体アイテム確認装置及びセルフサービス装置
EP06831386A EP1964076A1 (en) 2005-12-16 2006-12-14 Detecting improved quality counterfeit media items
PCT/GB2006/004670 WO2007068928A1 (en) 2005-12-16 2006-12-14 Detecting improved quality counterfeit media
EP06820517A EP1964075A1 (en) 2005-12-16 2006-12-14 Detecting improved quality counterfeit media
CN2006800473687A CN101366061B (zh) 2005-12-16 2006-12-14 检测改进质量的伪造介质
PCT/GB2006/004676 WO2007068930A1 (en) 2005-12-16 2006-12-14 Detecting improved quality counterfeit media items
PCT/GB2006/004663 WO2007068923A1 (en) 2005-12-16 2006-12-14 Processing images of media items before validation
BRPI0620625-5A BRPI0620625A2 (pt) 2005-12-16 2006-12-14 detecção de mìdia falsificada de qualidade aperfeiçoada
EP06820512A EP1964074A1 (en) 2005-12-16 2006-12-14 Processing images of media items before validation
BRPI0620308-6A BRPI0620308A2 (pt) 2005-12-16 2006-12-14 processamento de imagem de itens de mìdia antes da validação
CN2006800475165A CN101331527B (zh) 2005-12-16 2006-12-14 在验证之前处理介质对象的图像
US11/639,593 US20070154078A1 (en) 2005-12-16 2006-12-15 Processing images of media items before validation
US11/639,597 US20070154079A1 (en) 2005-12-16 2006-12-15 Media validation
US11/639,576 US8086017B2 (en) 2005-12-16 2006-12-15 Detecting improved quality counterfeit media

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US30553705A 2005-12-16 2005-12-16
US11/366,147 US20070140551A1 (en) 2005-12-16 2006-03-02 Banknote validation

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US30553705A Continuation-In-Part 2005-12-16 2005-12-16

Related Child Applications (3)

Application Number Title Priority Date Filing Date
US11/639,576 Continuation-In-Part US8086017B2 (en) 2005-12-16 2006-12-15 Detecting improved quality counterfeit media
US11/639,597 Continuation-In-Part US20070154079A1 (en) 2005-12-16 2006-12-15 Media validation
US11/639,593 Continuation-In-Part US20070154078A1 (en) 2005-12-16 2006-12-15 Processing images of media items before validation

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US20070140551A1 true US20070140551A1 (en) 2007-06-21

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US11/366,147 Abandoned US20070140551A1 (en) 2005-12-16 2006-03-02 Banknote validation
US11/639,576 Active 2028-12-14 US8086017B2 (en) 2005-12-16 2006-12-15 Detecting improved quality counterfeit media
US11/639,597 Abandoned US20070154079A1 (en) 2005-12-16 2006-12-15 Media validation
US11/639,593 Abandoned US20070154078A1 (en) 2005-12-16 2006-12-15 Processing images of media items before validation

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US11/639,576 Active 2028-12-14 US8086017B2 (en) 2005-12-16 2006-12-15 Detecting improved quality counterfeit media
US11/639,597 Abandoned US20070154079A1 (en) 2005-12-16 2006-12-15 Media validation
US11/639,593 Abandoned US20070154078A1 (en) 2005-12-16 2006-12-15 Processing images of media items before validation

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US (4) US20070140551A1 (ja)
EP (4) EP1964073A1 (ja)
JP (4) JP5219211B2 (ja)
BR (4) BRPI0619845A2 (ja)
WO (4) WO2007068867A1 (ja)

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