The objective of this work is to detect counterfeit banknotes using image pattern classification techniques. The color scanner makes it easier to produce counterfeit banknotes. So it is important to find an efficient method to detect counterfeit banknotes. In this work, a method for automated banknote authentication is proposed, which segments the whole banknote into many regions, and then builds individual classifiers on each region. Firstly, the banknote is segmented into different number of partitions. Then the luminance histogram and texture features are extracted from each partition of the banknote. The features extracted from each partition are then used to classify the banknotes using multiple support vector machines. The result is whether the currency is genuine or counterfeit.

Support Vector Machine, Counterfeit Banknote, Luminance Histogram, Texture Features

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