An Efficient Multiple Classifier Based on Fast RBFN for Biometric Identification

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

In this present paper a modified Radial Basis Function Network (RBFN) based multiple classifiers for person identification has been designed and developed. This multiple classification system comprises of three individual classifiers based on a modified RBFN using Optimal Clustering Algorithm (OCA). These three individual classifiers perform Fingerprint, Iris and Face identification respectively and the super classifier performs the final identification based on voting logic. The technique of using the modified RBFN and OCA to design each classifier for fingerprint, iris and face is efficient, effective and fast. Also the accuracies of the classifiers are substantially moderate and the recognition times are quite low.

Keywords

Multiple Classifier Fingerprint Identification Iris Identification Face Identification OCA RBFN BP Learning Holdout method Accuracy 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentNIT DurgapurDurgapurIndia

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