Title :
Paper Currency Verification with Support Vector Machines
Author :
Chang, Chin-Chen ; Yu, Tai-Xing ; Yen, Hsuan-Yen
Abstract :
Distinct from conventional techniques where the neural network (NN) is employed to solve the problem of paper currency verification, in this paper, we shall present a novel method by applying the support vector machine (SVM) approach to distinguish counterfeit banknotes from genuine ones. On the basis of the statistical learning theory, SVM has better generalization ability and higher performance especially when it comes to pattern classification. Besides, discrete wavelet transformation (DWT) will also be applied so as to reduce the input scale of SVM. Finally, the results of our experiment will show that the proposed method does achieve very good performance.
Keywords :
discrete wavelet transforms; learning (artificial intelligence); neural nets; program verification; statistical analysis; support vector machines; SVM; discrete wavelet transformation; neural network; paper currency verification; statistical learning theory; support vector machines; Counterfeiting; Discrete wavelet transforms; IP networks; Neural networks; Pattern classification; Pattern recognition; Printing; Statistical learning; Support vector machine classification; Support vector machines; Support vector machine; banknote verification; paper currency verification;
Conference_Titel :
Signal-Image Technologies and Internet-Based System, 2007. SITIS '07. Third International IEEE Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3122-9
DOI :
10.1109/SITIS.2007.146