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Performance Analysis of Iris Recognition System

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Data and Communication Networks

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 847))

Abstract

A biometric system offers automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Although iris identification system is based on pattern recognition technique but due to poor iris boundary detection and high computational time in previous work, we used neural network and discriminant machine learning technique to obtained high accuracy. In this work, we implement neural network and discriminant analysis of machine learning method for iris recognition in iris images to implement in day-to-day life, using MATLAB 2016a. The emphasis will be only on the software for performing recognition and not hardware for capturing an eye image. The proposed method gives better recognition rate than SVM technique with less computational complexity. Neural network and discriminant methods are used for matching and finding recognition accuracy. Thus, the accuracy obtained from neural network is 94.44%, whereas from discriminant analysis the accuracy obtained is 99.99%.

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Author information

Authors and Affiliations

  1. Galgotias University, Greater Noida, 201310, India

    Ruqaiya Khanam, Zohreen Haseen, Nighat Rahman & Jugendra Singh

Authors
  1. Ruqaiya Khanam
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  2. Zohreen Haseen
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  3. Nighat Rahman
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  4. Jugendra Singh
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Corresponding author

Correspondence to Ruqaiya Khanam .

Editor information

Editors and Affiliations

  1. University of Canberra, Adelaide, SA, Australia

    Lakhmi C. Jain

  2. Aurel Vlaicu University of Arad, Arad, Romania

    Valentina E. Balas

  3. School of Computing Science and Engineering, Galgotias University, Greater Noida, Uttar Pradesh, India

    Prashant Johri

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Khanam, R., Haseen, Z., Rahman, N., Singh, J. (2019). Performance Analysis of Iris Recognition System. In: Jain, L., E. Balas, V., Johri, P. (eds) Data and Communication Networks. Advances in Intelligent Systems and Computing, vol 847. Springer, Singapore. https://doi.org/10.1007/978-981-13-2254-9_14

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  • DOI: https://doi.org/10.1007/978-981-13-2254-9_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2253-2

  • Online ISBN: 978-981-13-2254-9

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