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An Efficient Indexing Scheme for Iris Biometric Using K-d-b Trees

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Intelligent Computing Theories and Technology (ICIC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7996))

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Abstract

In this paper, an indexing approach is proposed for clustered SIFT keypoints using k-d-b tree. K-d-b tree combines the multidimensional capability of k-d tree and balancing efficiency of B tree. During indexing phase, each cluster center is used to traverse the region pages of k-d-b tree to reach an appropriate point page for insertion. For m cluster centers, m such trees are constructed. Insertion of a node into k-d-b tree is dynamic that generates balanced data structure and incorporates deduplication check as well. For retrieval, range search approach is used which finds the intersection of probe cluster center with each region page being traversed. The iris identifiers on the point page referenced by probe iris image are retrieved. Results are obtained on publicly available BATH and CASIA Iris Image Database Version 3.0. Empirically it is found that k-d-b tree is preferred over state-of-the-art biometric database indexing approaches.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Rourkela, Odisha, 769008, India

    Hunny Mehrotra & Banshidhar Majhi

Authors
  1. Hunny Mehrotra
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  2. Banshidhar Majhi
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Editor information

Editors and Affiliations

  1. Machine Learning and Systems Biology Laboratory, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Electrical Engineering, University of Ulsan, 680-749 #7-413, San 29, Muger Dong, Ulsan, South Korea

    Kang-Hyun Jo

  3. Guangxi University for Nationalities, 530006, Nanning, Guangxi, China

    Yong-Quan Zhou

  4. School of Computer Science and Engineering, Inha University, 402-751, Incheon, South Korea

    Kyungsook Han

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Mehrotra, H., Majhi, B. (2013). An Efficient Indexing Scheme for Iris Biometric Using K-d-b Trees. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_55

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  • DOI: https://doi.org/10.1007/978-3-642-39482-9_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39481-2

  • Online ISBN: 978-3-642-39482-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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