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Fingerprint Classification by Filter Bank Approach Using Evolutionary ANN

  • Annapurna Mishra
  • Satchidananda Dehuri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)

Abstract

This paper presents an evolutionary artificial neural network for classification of fingerprints in the area of biometric recognition. An efficient way for feature extraction from the fingerprints using a Gabor filter bank has been studied very rigorously and extracted potentially useful features. Here five classes of fingerprints have been taken into consideration. We have conducted experimental study to prove the effectiveness of the method on NIST-9 database. It is evident from the results that the method is effective in classifying the fingerprints with a varying degree of accuracy vis-à-vis to the different parameters setting.

Keywords

Classification Gabor filter EANN Feature extraction Evolutionary 

Notes

Acknowledgements

The first author would like to thank the technical support of Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringSilicon Institute of TechnologyBhubaneswarIndia
  2. 2.Department of Information and Communication TechnologyFakir Mohan UniversityBalasoreIndia

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