Heap-Based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms

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


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.


Coprocessors Xeon Phi MCC Fingerprint 



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.


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratory of Technological Research in Pattern Recognition (LITRP), Department of Computer Science and Industries, Faculty of Engineering ScienceUniversidad Católica del MauleTalcaChile
  2. 2.Kunert Business Software GmbH (KBS-Leipzig)LeipzigGermany
  3. 3.Universitat Autònoma de BarcelonaBarcelonaSpain
  4. 4.Data Mining and Modelling for Biomedicine GroupVIB Center for Inflammation ResearchGhentBelgium
  5. 5.Department of Applied Mathematics, Computer Science and StatisticsGhent UniversityGhentBelgium

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