Fingerprint identification using distributed computing.
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Date
2012
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Abstract
Biometric systems such as face, palm and fingerprint recognition are very computationally
expensive. The ever growing biometric database sizes have posed a need
for faster search algorithms. High resolution images are expensive to process and
slow down less powerful extraction algorithms. There is an apparent need to improve
both the signal processing and the searching algorithms. Researchers have continually
searched for new ways of improving the recognition algorithms in order to keep up
with the high pace of the scientific and information security world. Most such developments,
however, are architecture- or hardware-specific and do not port well to other
platforms.
This research proposes a cheaper and portable alternative. With the use of the Single
Program Multiple Data programming architecture, a distributed fingerprint recognition
algorithm is developed and executed on a powerful cluster. The first part in the
parallelization of the algorithm is distributing the image enhancement algorithm which
comprises of a series of computationally intensive image processing operations. Different
processing elements work concurrently on different parts of the same image in
order to speed up the processing. The second part of parallelization speeds up searching/
matching through a parallel search. A database is partitioned as evenly as possible
amongst the available processing nodes which work independently to search their respective
partitions. Each processor returns a match with the highest similarity score
and the template with the highest score among those returned is returned as match
given that the score is above a certain threshold. The system performance with respect
to response time is then formalized in a form of a performance model which can be
used to predict the performance of a distributed system given network parameters and
number of processing nodes.
The proposed algorithm introduces a novel approach to memory distribution of
block-wise image processing operations and discusses three different ways to process
pixels along the partitioning axes of the distributed images. The distribution and parallelization
of the recognition algorithm gains up to as much as 12.5 times performance
in matching and 10.2 times in enhancement.
Description
Thesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2012.
Keywords
Electronic data processing--Distributed processing., Fingerprints--Identification--Automation., Theses--Computer engineering.