Implementing a reliable neuro-classifier for paper currency using PCA algorithm

In this paper we present a PCA based method for increasing the reliability of paper currency recognition machines. The system is intended for classifying any kind of currency but in this work we examine only different kinds of US dollar (totally 10 bill types). The data is acquired through some advanced sensors and after preprocessing come as an array of pixels. The PCA algorithm is used to extract the main features of data and reducing the data size. An LVQ network model is applied as the main classifier of system. By defining a specific criteria for rating the reliability, we evaluate the reliability of system for 1,200 test data. The result shows that reliability is increased up to 95% when the number of PCA components as well as number of LVQ codebooks are taken properly.