Banknote validation

Abstract

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. In a preferred embodiment the banknote validator is incorporated in a self-service apparatus such as an automated teller machine.

1 . A method of creating a classifier for banknote validation said method comprising the steps of: 
 (i) accessing a training set of banknote images;    (ii) creating a segmentation template using the training set images;    (iii) segmenting each of the training set images using the segmentation template;    (iv) extracting one or more features from each segment in each of the training set images; and    (v) forming the classifier using the feature information;    wherein the segmentation template is created on the basis of information from all images in the training set.    
   
   
       2 . A method as claimed in  claim 1  wherein the information from all images in the training set comprises morphological information.  
   
   
       3 . A method as claimed in  claim 1  wherein 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.  
   
   
       4 . A method as claimed in  claim 2 , wherein the information from all the images comprises pixel intensity profiles.  
   
   
       5 . A method as claimed in  claim 1 , wherein 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.  
   
   
       6 . A method as claimed in  claim 1 , wherein the classifier is a one-class classifier.  
   
   
       7 . A method as claimed in  claim 1 , wherein the classifier is a statistical one-class classifier.  
   
   
       8 . A method as claimed in  claim 7  and wherein 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.  
   
   
       9 . A method as claimed in  claim 1 , which further comprises using a feature selection algorithm to select one or more types of feature to use in step (iv) of extracting features.  
   
   
       10 . A method as claimed in  claim 1 , which further comprises forming the classifier on the basis of specified information about a particular denomination and currency of banknotes.  
   
   
       11 . A method as claimed in  claim 1 , which further comprises combining classifiers where necessary in step (v) of forming the classifier.  
   
   
       12 . An apparatus for creating a banknote classifier comprising: 
 (i) an input arranged to access a training set of banknote images;    (ii) a processor arranged to create a segmentation template using the training set images;    (iii) a segmentor arranged to segmenting each of the training set images using the segmentation template;    (iv) a feature extractor arranged to extract one or more features from each segment in each of the training set images; and    (v) classification forming means arranged to form the classifier using the feature information;    wherein the processor is arranged to create the segmentation template on the basis of information from all images in the training set.    
   
   
       13 . A banknote validator comprising: 
 (i) an input arranged to receive at least one image of a banknote to be validated;    (ii) a segmentation template;    (iii) a processor arranged to segment the image of the banknote using the segmentation template;    (iv) a feature extractor arranged to extract one or more features from each segment of the banknote image;    (v) a classifier arranged to classify the banknote as being either valid or not on the basis of the extracted features;    wherein the segmentation template is formed on the basis of information about each of a set of training images of banknotes.    
   
   
       14 . A banknote validator as claimed in  claim 13  wherein the information about each of a set of training images comprises morphological information.  
   
   
       15 . A banknote validator as claimed in  claim 13 , wherein the information about each of a set of training images comprises information about a pixel at the same location in each of the training set images.  
   
   
       16 . A banknote validator as claimed in  claim 13 , wherein the information about each of a set of training images comprises pixel intensity profiles.  
   
   
       17 . A banknote validator as claimed in  claim 13 , wherein the classifier is a one-class classifier.  
   
   
       18 . A banknote validator as claimed in  claim 13 , wherein the classifier is a statistical one-class classifier.  
   
   
       19 . A banknote validator as claimed in  claim 13 , which further comprises a plurality of classifiers and a combiner arranged to combine the results of each of the classifiers.  
   
   
       20 . A method of validating a banknote comprising: 
 (i) accessing at least one image of a banknote to be validated;    (ii) accessing a segmentation template;    (iii) segmenting the image of the banknote using the segmentation template;    (iv) extracting features from each segment of the banknote image;    (v) classifying the banknote as being either valid or not on the basis of the extracted features using a classifier;    wherein the segmentation template is formed on the basis of information about each of a set of training images of banknotes.    
   
   
       21 . A method as claimed in  claim 20 , wherein said classifier is a one-class classifier.  
   
   
       22 . A method as claimed in  claim 20 , wherein said classifier is a statistical classifier.  
   
   
       23 . A computer program comprising computer program code means adapted to perform method of creating a classifier for banknote validation said method comprising the steps of: (i) accessing a training set of banknote images; (ii) creating a segmentation template using the training set images; (iii) segmenting each of the training set images using the segmentation template; (iv) extracting one or more features from each segment in each of the training set images; and (v) forming the classifier using the feature information; wherein the segmentation template is created on the basis of information from all images in the training set.  
   
   
       24 . A computer program as claimed in  claim 23  embodied on a computer readable medium.