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Local feature based retrieval approach for iris biometrics

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Abstract

This paper proposes an efficient retrieval approach for iris using local features. The features are extracted from segmented iris image using scale invariant feature transform (SIFT). The keypoint descriptors extracted from SIFT are clustered into m groups using k-means. The idea is to perform indexing of keypoints based on descriptor property. During database indexing phase, k-d tree k-dimensional tree is constructed for each cluster center taken from N iris images. Thus for m clusters, m such k-d trees are generated denoted as t i , where 1 ⩽ i ⩽ m. During the retrieval phase, the keypoint descriptors from probe iris image are clustered into m groups and ith cluster center is used to traverse corresponding t i for searching. k nearest neighbor approach is used, which finds p neighbors from each tree (t i ) that falls within certain radius r centered on the probe point in k-dimensional space. Finally, p neighbors from m trees are combined using union operation and top S matches (S ⊆ (m× p)) corresponding to query iris image are retrieved. The proposed approach has been tested on publicly available databases and outperforms the existing approaches in terms of speed and accuracy.

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Authors and Affiliations

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

    Hunny Mehrotra & Banshidhar Majhi

Authors
  1. Hunny Mehrotra
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  2. Banshidhar Majhi
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Correspondence to Hunny Mehrotra.

Additional information

Hunny Mehrotra is presently pursuing PhD from Department of Computer Science and Engineering, National Institute of Technology Rourkela, India. She has published more then 30 papers in refereed international conferences and journals. Her area of research includes biometrics, image processing, and computer vision. She has been conferred with various prestigious awards like Google India women in engineering award in 2010, innovative student project award in 2010 by INAE, and fellowship from DST under women scientist scheme in 2012.

Banshidhar Majhi is a professor in Department of Computer Science and Engineering, National Institute of Technology Rourkela, India since 2006. He has 20 years of teaching and research experience. He has published several articles in refereed journals and international conferences. He has worked on several government funded projects. His area of interest includes data structures, image processing, cryptography, biometrics, parallel processing, and soft computing.

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Mehrotra, H., Majhi, B. Local feature based retrieval approach for iris biometrics. Front. Comput. Sci. 7, 767–781 (2013). https://doi.org/10.1007/s11704-013-3073-7

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  • DOI: https://doi.org/10.1007/s11704-013-3073-7

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