A reliable method for recognition of paper currency by approach to local PCA

Author

Ahmadi, Ali ; Omatu, Sigeru ; Kosaka, Toshihisa

Author_Institution

Dept. of Comput. & Syst. Sci., Osaka Prefectural Univ., Japan

Volume

2

fYear

2003

fDate

20-24 July 2003

Firstpage

1258

Abstract

This paper addresses the reliability of neuro-classifiers for paper currency recognition. A local principal component analysis (PCA) method is applied to remove non-linear dependencies among variables and extract the main principal features of data. At first the data space is partitioned into regions by using a self-organizing map (SOM) model and then the PCA is performed in each region. A learning vector quantization (LVQ) network is employed as the main classifier of the system. By defining a new algorithm for rating the reliability and using a set of test data, we estimate the reliability of the system. The experimental results taken from 1,200 samples of US dollar bills show that the reliability is increased up to 100% when the number of regions as well as the number of codebook vectors in the LVQ classifier is taken properly.

Keywords

feature extraction; image recognition; learning (artificial intelligence); principal component analysis; self-organising feature maps; vector quantisation; US dollar bills; codebook vectors; data space; learning vector quantization network; local principal component analysis; neuro-classifiers; paper currency; self-organizing map model; system reliability; Clustering algorithms; Data mining; Feature extraction; Neural networks; Partitioning algorithms; Principal component analysis; Robustness; Sensor arrays; System testing; Vector quantization;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2003. Proceedings of the International Joint Conference on

ISSN

1098-7576

Print_ISBN

0-7803-7898-9

Type

conf

DOI

10.1109/IJCNN.2003.1223874

Filename

1223874