International Journal of Computational Intelligence Systems (Aug 2020)

Novel Optimization Based Hybrid Self-Organizing Map Classifiers for Iris Image Recognition

  • J. Jenkin Winston,
  • Gul Fatma Turker,
  • Utku Kose,
  • D. Jude Hemanth

DOI
https://doi.org/10.2991/ijcis.d.200721.001
Journal volume & issue
Vol. 13, no. 1

Abstract

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The concern over security in all fields has intensified over the years. The prefatory phase of providing security begins with authentication to provide access. In many scenarios, this authentication is provided by biometric systems. Moreover, the threat of pandemic has made the people to think of hygienic systems which are noninvasive. Iris image recognition is one such noninvasive biometric system that can provide automated authentication. Self-organizing map is an artificial neural network which helps in iris image recognition. This network has the ability to learn the input features and perform classification. However, from the literature it is observed that the performance of this classifier has scope for refinement to yield better classification. In this paper, heterogeneous methods are adapted to improve the performance of the classifier for iris image recognition. The heterogeneous methods involve the application of Gravity Search Optimization, Teacher Learning Based Optimization, Whale Optimization and Gray Wolf Optimization in the training process of the self-organizing map classifier. This method was tested on iris images from IIT-Delhi database. The results of the experiment show that the proposed method performs better.

Keywords