Intelligent Iris Recognition Using Neural Networks

Muhammad Sarfraz*, Mohamed Deriche, Muhammad Moinuddin, Syed Saad Azhar Ali

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Scopus citations

Abstract

The objective of this chapter is to present a thorough literature survey on iris recognition work. It also presents a novel approach to iris recognition based on feed-forward neural networks. The features used in this approach are based on the contour of the iris-pupil boundary obtained from radius vector functions and named the 'iris signature'. The proposed technique used is translation, rotation and scale invariant. The classification is performed using two different neural network structures, the Multilayer Feed-forward Neural Network (MFNN) and the Radial Basis Function Neural Network (RBFNN). For feature extraction, the following steps are used: 1. The process starts by locating the outer and inner boundaries of the iris. 2. The second step is to find the contour of the inner boundary, i.e. the iris-pupil boundary. 3. Finally, the iris is represented by 'radius vector functions' and the representation is named the 'iris signature'.

Original languageEnglish
Title of host publicationComputer-Aided Intelligent Recognition Techniques and Applications
PublisherJohn Wiley & Sons, Ltd
Pages145-167
Number of pages23
ISBN (Print)0470094141, 9780470094143
DOIs
StatePublished - 20 Dec 2005

Keywords

  • Activation function
  • Back propagation (BP) algorithm
  • Extracting Iris Features
  • MFNN and RBFNN
  • Multilayer Perceptron (MLP)
  • Neural Pattern Recognition (NeurPR) approach
  • feed-forward neural network (MFNN)
  • radial basis function neural network (RBFNN)

ASJC Scopus subject areas

  • General Engineering

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