Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms

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 (M...

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Detalles Bibliográficos
Autores principales: Barrientos, Ricardo J., Hernández-García, Ruber, Ortega, Kevin, Luque Fadón, Emilio, Peralta, Daniel
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2019
Materias:
Ciencias Informáticas
Coprocessors
Xeon Phi
MCC
Fingerprint
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/80382
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-80382
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Coprocessors
Xeon Phi
MCC
Fingerprint
spellingShingle Ciencias Informáticas
Coprocessors
Xeon Phi
MCC
Fingerprint
Barrientos, Ricardo J.
Hernández-García, Ruber
Ortega, Kevin
Luque Fadón, Emilio
Peralta, Daniel
Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms
topic_facet Ciencias Informáticas
Coprocessors
Xeon Phi
MCC
Fingerprint
description 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.
format Objeto de conferencia
Objeto de conferencia
author Barrientos, Ricardo J.
Hernández-García, Ruber
Ortega, Kevin
Luque Fadón, Emilio
Peralta, Daniel
author_facet Barrientos, Ricardo J.
Hernández-García, Ruber
Ortega, Kevin
Luque Fadón, Emilio
Peralta, Daniel
author_sort Barrientos, Ricardo J.
title Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms
title_short Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms
title_full Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms
title_fullStr Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms
title_full_unstemmed Heap-based Algorithms to Accelerate Fingerprint Matching on Parallel Platforms
title_sort heap-based algorithms to accelerate fingerprint matching on parallel platforms
publishDate 2019
url http://sedici.unlp.edu.ar/handle/10915/80382
work_keys_str_mv AT barrientosricardoj heapbasedalgorithmstoacceleratefingerprintmatchingonparallelplatforms
AT hernandezgarciaruber heapbasedalgorithmstoacceleratefingerprintmatchingonparallelplatforms
AT ortegakevin heapbasedalgorithmstoacceleratefingerprintmatchingonparallelplatforms
AT luquefadonemilio heapbasedalgorithmstoacceleratefingerprintmatchingonparallelplatforms
AT peraltadaniel heapbasedalgorithmstoacceleratefingerprintmatchingonparallelplatforms
bdutipo_str Repositorios
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