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Iris Recognition Using Textural Edgeness Features

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Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 43))

Abstract

A method for feature extraction from an iris image based on the concept of textural edgeness is presented in this paper. Here for authentication purpose we have used two textural edgeness features namely: (1) a modified version of Gray Level Auto Correlation (GLAC) and (2) Scale Invariant Feature transform (SIFT) descriptors over dense grids in the image domain. Extensive experimental results using MMU1 and IITD iris databases demonstrate the effectiveness of the proposed system.

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Author information

Authors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, Baranagar, India

    Saiyed Umer & Bhabatosh Chanda

  2. Department of Information Technology, Jadavpur University, Kolkata, India

    Bibhas Chandra Dhara

Authors
  1. Saiyed Umer
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  2. Bibhas Chandra Dhara
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  3. Bhabatosh Chanda
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Corresponding author

Correspondence to Saiyed Umer .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Liverpool Hope University, Liverpool, United Kingdom

    Atulya Nagar

  2. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, Odisha, India

    Durga Prasad Mohapatra

  3. Computer Science & Engineering, University of Calcutta, Kolkata, West Bengal, India

    Nabendu Chaki

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© 2016 Springer India

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Umer, S., Dhara, B.C., Chanda, B. (2016). Iris Recognition Using Textural Edgeness Features. In: Nagar, A., Mohapatra, D., Chaki, N. (eds) Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics. Smart Innovation, Systems and Technologies, vol 43. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2538-6_29

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  • DOI: https://doi.org/10.1007/978-81-322-2538-6_29

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2537-9

  • Online ISBN: 978-81-322-2538-6

  • eBook Packages: EngineeringEngineering (R0)

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