research-article

Coarse-Grained Architecture for Fingerprint Matching

Authors:

Zhiqiang LiuAuthors Info & Claims

ACM Transactions on Reconfigurable Technology and Systems (TRETS), Volume 9, Issue 2

Article No.: 12, Pages 1 - 15

https://doi.org/10.1145/2791296

Published: 17 December 2015 Publication History

Abstract

Fingerprint matching is a key procedure in fingerprint identification applications. The minutiae-based fingerprint matching algorithm is one of the most typical algorithms achieving a reasonably correct recognition rate. This study proposes a coarse-grained parallel architecture called fingerprint matching core (FMC) to accelerate fingerprint matching. The proposed architecture has a two-level parallel structure (i.e., parallel among groups (PAG) and parallel in group (PIG)). A multirequest controller is added to the PAG structure to obtain a concurrent operation of the multiple processing element group (PEG). The DDR3 controller is used in the PIG structure to read eight minutiae from eight different fingerprints and realize the simultaneous computation of the eight PEs. The whole system is implemented on a Xilinx FPGA board with a Virtex VII XC7VX485T chip. The 16-PEG FMC achieves a throughput of about 9.63 million fingerprint pairs per second, which is larger than that achieved on a Tesla K20c platform. The software execution times are also measured on the 2.93GHz Intel Xeon 5670, 2.3GHz AMD Opteron(tm) Processor 6376, and Tesla K20c platforms. The Intel Xeon 5670 has two processors with 12 cores, and the AMD Opteron(tm) Processor 6376 has two processors with 16 cores. Moreover, the throughput is about 31 times that achieved on a 2.93GHz Intel Xeon 5670 single core.

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Cited By

Shafiq MTaj IGhafoor MTariq SAbbas AZomaya A(2020)Accelerating fingerprint identification using FPGA for large-scale applicationsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2020.03.007141(35-48)Online publication date: Jul-2020
https://doi.org/10.1016/j.jpdc.2020.03.007
Bian WXu DLi QCheng YJie BDing X(2019)A Survey of the Methods on Fingerprint Orientation Field EstimationIEEE Access10.1109/ACCESS.2019.29036017(32644-32663)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2903601

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Published In

cover image ACM Transactions on Reconfigurable Technology and Systems

ACM Transactions on Reconfigurable Technology and Systems Volume 9, Issue 2

Special Section on RAW2014

February 2016

146 pages

ISSN:1936-7406

EISSN:1936-7414

DOI:10.1145/2854101

Editor:
Steve Wilton
Department of Electrical and Computer Engineering/University of British Columbia/Kaiser

Issue’s Table of Contents

Copyright © 2015 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 December 2015

Accepted: 01 June 2015

Revised: 01 May 2015

Received: 01 November 2014

Published in TRETS Volume 9, Issue 2

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National High Technology Research and Development Program of China
National Science Foundation of China

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Cited By

Shafiq MTaj IGhafoor MTariq SAbbas AZomaya A(2020)Accelerating fingerprint identification using FPGA for large-scale applicationsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2020.03.007141(35-48)Online publication date: Jul-2020
https://doi.org/10.1016/j.jpdc.2020.03.007
Bian WXu DLi QCheng YJie BDing X(2019)A Survey of the Methods on Fingerprint Orientation Field EstimationIEEE Access10.1109/ACCESS.2019.29036017(32644-32663)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2903601

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