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Heap-Based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms

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Cloud Computing and Big Data (JCC&BD 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1050))

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Abstract

Nowadays, fingerprint is the most used biometric trait for individuals identification. In this area, the state-of-the-art algorithms are very accurate, but when the database contains millions of identities, an acceleration of the algorithm is required. From these algorithms, Minutia Cylinder-Code (MCC) stands out for its good results in terms of accuracy, however its efficiency in computational time is not high. In this work, we propose to use two different parallel platforms to accelerate fingerprint matching process by using MCC: (1) a multi-core server, and (2) a Xeon Phi coprocessor. Our proposal is based on heaps as auxiliary structure to process the global similarity of MCC. As heap-based algorithms are exhaustive (all the elements are accessed), we also explored the use an indexing algorithm to avoid comparing the query against all the fingerprints of the database. Experimental results show an improvement up to 97.15x of speed-up, which is competitive compared to other state-of-the-art algorithms in GPU and FPGA. To the best of our knowledge, this is the first work for fingerprint identification using a Xeon Phi coprocessor.

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Acknowledgement

This research was partially funded by Project CONICYT FONDEF/Cuarto Concurso IDeA en dos Etapas del Fondo de Fomento al Desarrollo Científico y Tecnológico, Programa IDeA, FONDEF/CONICYT 2017 ID17i10254. D. Peralta is a Postdoctoral Fellow of the Research Foundation of Flanders.

Author information

Authors and Affiliations

  1. Laboratory of Technological Research in Pattern Recognition (LITRP), Department of Computer Science and Industries, Faculty of Engineering Science, Universidad Católica del Maule, Talca, Chile

    Ricardo J. Barrientos & Ruber Hernández-García

  2. Kunert Business Software GmbH (KBS-Leipzig), Leipzig, Germany

    Kevin Ortega

  3. Universitat Autònoma de Barcelona, Barcelona, Spain

    Emilio Luque

  4. Data Mining and Modelling for Biomedicine Group, VIB Center for Inflammation Research, Ghent, Belgium

    Daniel Peralta

  5. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium

    Daniel Peralta

Authors
  1. Ricardo J. Barrientos
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  2. Ruber Hernández-García
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  3. Kevin Ortega
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  4. Emilio Luque
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  5. Daniel Peralta
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Corresponding author

Correspondence to Ricardo J. Barrientos .

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Editors and Affiliations

  1. III-LIDI, Facultad de Informatica, Universidad Nacional de La Plata, La Plata , Argentina

    Marcelo Naiouf

  2. III-LIDI, Facultad de Informatica, Universidad Nacional de La Plata, La Plata, Argentina

    Franco Chichizola

  3. III-LIDI, Facultad de Informatica, Universidad Nacional de La Plata, La Plata, Argentina

    Enzo Rucci

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Barrientos, R.J., Hernández-García, R., Ortega, K., Luque, E., Peralta, D. (2019). Heap-Based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms. In: Naiouf, M., Chichizola, F., Rucci, E. (eds) Cloud Computing and Big Data. JCC&BD 2019. Communications in Computer and Information Science, vol 1050. Springer, Cham. https://doi.org/10.1007/978-3-030-27713-0_6

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  • DOI: https://doi.org/10.1007/978-3-030-27713-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27712-3

  • Online ISBN: 978-3-030-27713-0

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