Employing optimized combinations of one-class classifiers for automated currency validation.
Automated currency validation requires a decision to be made regarding the authenticity of a banknote presented to the validation system. This decision often has to be made with little or no information regarding the characteristics of possible counterfeits as is the case for issues of new currency. A method for automated currency validation is presented which segments the whole banknote into different regions, builds individual classifiers on each region and then combines a small subset of the region specific classifiers to provide an overall decision. The segmentation and combination of region specific classifiers to provide optimized false positive and false negative rates is achieved by employing a genetic algorithm. Experiments based on high value notes of Sterling currency were carried out to assess the effectiveness of the proposed solution. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
|Title:||Employing optimized combinations of one-class classifiers for automated currency validation|
|Keywords:||novelty detection, one-class classifier, classifier combination, automated currency validation, genetic algorithm|
|UCL classification:||UCL > School of BEAMS > Faculty of Maths and Physical Sciences
UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
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