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Fingerprint Image Segmentation Based on Gaussian-Hermite Moments

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Advanced Data Mining and Applications (ADMA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3584))

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Abstract

An important step in automatic fingerprint recognition systems is the segmentation of fingerprint images. In this paper, we present an adaptive algorithm based on Gaussian-Hermite moments for non-uniform background removing in fingerprint image segmentation. Gaussian-Hermite moments can better separate image features based on different modes. We use Gaussian-Hermite moments of different orders to separate background and foreground of fingerprint image. Experimental results show that the use of Gaussian-Hermite moments makes a significant improvement for the segmentation of fingerprint images.

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Author information

Authors and Affiliations

  1. Institute EGID-Bordeaux 3, University of Michel de Montaigne - Bordeaux 3, 1 Allée Daguin, 33607, Pessac cedex, France

    Lin Wang & Mo Dai

  2. Department of Mathematics, Guizhou University for Ethnic Minorities, Guiyang, 550025, China

    Hongmin Suo

Authors
  1. Lin Wang
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  2. Hongmin Suo
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  3. Mo Dai
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, Brisbane, Queensland, Australia

    Xue Li

  2. The State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 430072, Wuhan, China

    Shuliang Wang

  3. School of ITEE, The Univ of Queensland, St. Lucia, 4072, QLD, Australia

    Zhao Yang Dong

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© 2005 Springer-Verlag Berlin Heidelberg

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Wang, L., Suo, H., Dai, M. (2005). Fingerprint Image Segmentation Based on Gaussian-Hermite Moments. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_54

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  • DOI: https://doi.org/10.1007/11527503_54

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27894-8

  • Online ISBN: 978-3-540-31877-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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