Iris recognition systems have recently become an attractive identification method because of their extremely high
accuracy. Most modern iris recognition systems are currently deployed on traditional sequential digital systems, such as
a computer. However, modern advancements in configurable hardware, most notably Field-Programmable Gate Arrays
(FPGAs) have provided an exciting opportunity to discover the parallel nature of modern image processing algorithms.
In this study, iris matching, a repeatedly executed portion of a modern iris recognition algorithm is parallelized on an
FPGA system. We demonstrate a 19 times speedup of the parallelized algorithm on the FPGA system when compared to
a state-of-the-art CPU-based version.
Iris recognition is an increasingly popular biometric due to its relative ease of use and high reliability. However, commercially available systems typically require on-axis images for recognition, meaning the subject is looking in the direction of the camera. The feasibility of using off-axis images is an important area of investigation for iris systems with more flexible user interfaces. The authors present an analysis of two image transform processes for off-axis images and an analysis of the utility of correcting for cornea refraction effects. The performance is assessed on the U.S. Naval Academy iris image database using the Ridge Energy Direction recognition algorithm developed by the authors, as well as with a commercial implementation of the Daugman algorithm.
The iris contains fibrous structures of various sizes and orientations which can be used for human identification.
Drawing from a directional energy iris identification technique, this paper investigates the size, orientation, and location
of the iris structures that hold stable discriminatory information. Template height, template width, filter size, and the
number of filter orientations were investigated for their individual and combined impact on identification accuracy.
Further, the iris was segmented into annuli and radial sectors to determine in which portions of the iris the best
discriminatory information is found. Over 2 billion template comparisons were performed to produce this analysis.
The iris is currently believed to be the most accurate biometric for human identification. The majority of fielded iris
identification systems are based on the highly accurate wavelet-based Daugman algorithm. Another promising
recognition algorithm by Ives et al uses Directional Energy features to create the iris template. Both algorithms use
Hamming distance to compare a new template to a stored database. Hamming distance is an extremely fast computation,
but weights all regions of the iris equally. Work from multiple authors has shown that different regions of the iris contain
varying levels of discriminatory information. This research evaluates four post-processing similarity metrics for
accuracy impacts on the Directional Energy and wavelets based algorithms. Each metric builds on the Hamming distance
method in an attempt to use the template information in a more salient manner. A similarity metric extracted from the
output stage of a feed-forward multi-layer perceptron artificial neural network demonstrated the most promise. Accuracy
tables and ROC curves of tests performed on the publicly available Chinese Academy of Sciences Institute of
Automation database show that the neural network based distance achieves greater accuracy than Hamming distance at
every operating point, while adding less than one percent computational overhead.
The human iris is perhaps the most accurate biometric
for use in identification. Commercial iris recognition systems currently
can be found in several types of settings where a person’s
true identity is required: to allow passengers in some airports to be
rapidly processed through security; for access to secure areas; and
for secure access to computer networks. The growing employment
of iris recognition systems and the associated research to develop
new algorithms will require large databases of iris images. If the
required storage space is not adequate for these databases, image
compression is an alternative. Compression allows a reduction in
the storage space needed to store these iris images. This may, however,
come at a cost: some amount of information may be lost in the
process. We investigate the effects of image compression on the
performance of an iris recognition system. Compression is performed
using JPEG-2000 and JPEG, and the iris recognition algorithm
used is an implementation of the Daugman algorithm. The
imagery used includes both the CASIA iris database as well as the
iris database collected by the University of Bath. Results demonstrate
that compression up to 50:1 can be used with minimal effects
on recognition.
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