Efficient fingerprint matching using GPU

Graphical processing unit (GPU) has proven a beneficial tool in handling computationally intensive algorithms, by introducing massive parallelism in the calculations. In this study, an effective and low-cost fingerprint identification (FI) solution is proposed that can exploit the parallel computati...

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Bibliographic Details
Published in:IET image processing 2018-02, Vol.12 (2), p.274-284
Main Authors: Ghafoor, Mubeen, Iqbal, Shahzaib, Tariq, Syed Ali, Taj, Imtiaz A, Jafri, Noman M
Format: Article
Language:English
Subjects:
automated FI system
computationally intensive algorithms
data structures
fingerprint identification
fingerprint matching algorithm
GPU technology
graphical processing unit
graphics processing units
image coding
image matching
image sequences
loop unrolling
minutia neighbour‐based novel encoding algorithm
multithreading
multi‐threading
nested loop minimisation
open source matcher
parallel computational power
Research Article
sequential central processing
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Summary:Graphical processing unit (GPU) has proven a beneficial tool in handling computationally intensive algorithms, by introducing massive parallelism in the calculations. In this study, an effective and low-cost fingerprint identification (FI) solution is proposed that can exploit the parallel computational power of GPU proficiently. It is achieved by mapping a generalised minutia neighbour-based novel encoding and matching algorithm on low-cost GPU technology. The proposed solution achieves high accuracy in comparison with two open source matchers and it is shown to be scalable by comparing matching performance on different GPUs. The proposed GPU implementation employs multithreading and loop unrolling, which minimises the use of nested loops and avoids sequential matching of encoded minutia features. After a thorough and careful designing of data structures, memory transfers and computations, a GPU-based fingerprint matching system is developed. It achieves on average 50,196 fingerprint matches per second on a single GPU. As compared to the sequential central processing unit implementation, the proposed system achieves a speed up of around 92 times, while maintaining the accuracy. The proposed system with matcher integrated on GPU can be considered as a good, low-cost, robust and efficient solution for large-scale applications of automated FI systems.
ISSN:1751-9659
1751-9667
1751-9667
DOI:10.1049/iet-ipr.2016.1021