ISSN : 1796-203X
Volume : 2    Issue : 6    Date : August 2007

Low-Complexity Analysis of Repetitive Regularities for Biometric Applications
Lei Wang and Niral Patel
Page(s): 56-64
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Presented in this paper is a joint algorithm optimization and architecture design framework for
analysis of repetitive regularities. Two closely coupled algorithm optimization techniques, referred to
as the prime subspace periodicity transform (PSPT) and circular periodicity transform (CPT), are
developed that significantly reduce computational complexity while enable the extraction of a wide
spectrum of periodic features. The proposed PSPTCPT algorithms lead to a parallel and resource
sharing VLSI architecture. While most of the current systems rely on software solutions to
performance feature extraction, the performance benefits rendered by the proposed framework
show advantages in dealing with data-intensive computation for emerging applications in
biometrics and bioinformatics. The explosive growth in database complexity combined with demand
for fast analysis make the proposed framework a promising solution. Experimental results on an
iris identification system demonstrate up to 99.2% accuracy and 24.6% − 41.9% reduction in
computational complexity.

Index Terms
algorithm transformation, VLSI architecture, fast algorithm, iris identification, biometrics.