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Having an easier life by the help of developing
technologies forces people is more complicated
technological structure. In today’s world, security is
more important than ever. Dazziling developments in
technology arouse interest of scientists about human and
human behaviors and at the same time, give an
opportunity to people to apply their thoughts. Today, for
security needs, detailed researches are organized to set
up the most reliable system. Iris Recognition Security
System is one of the most reliable leading technologies
that most people are related. Iris recognition
technology combines computer vision, pattern
recognition, statistical inference, and optics. Its purpose
is real-time, high confidence recognition of a person's
identity by mathematical analysis of the random patterns
that are visible within the iris of an eye from some
distance. Because the iris is a protected internal organ
whose random texture is stable throughout life, it can
serve as a kind of living passport or a living password
that one need not remember but can always present.
Because the randomness of iris patterns has very high
dimensionality, recognition decisions are made with
confidence levels high enough to support rapid and
reliable exhaustive searches through national-sized
databases.
Artificial Neural Networks (ANNs) are programs
designed to simulate the way a simple biological nervous
system is believed to operate. They are based on
simulated nerve cells or neurons, which are joined
together in a variety of ways to form networks. These
networks have the capacity to learn, memorize and
create relationships amongst data. ANN is an
information-processing paradigm, implemented in
hardware or software that is modeled after the biological
processes of the brain. An ANN is made up of a
collection of highly interconnected nodes, called neurons
or processing elements. A node receives weighted inputs
from other nodes, sums these inputs, and propagates this
sum through a function to other nodes. This process is
analogous to the actions of a biological neuron. An ANN
learns by example. In a biological brain, learning is
accomplished as the strengths of the connections
between nodes are adjusted. This is true for ANN’s also,
as these strengths are captured by the weights between
the nodes. ANN’s most important advantage is that they
can be used to solve problems of considerable
complexity; problems that do not have an algorithmic
solution or for which such a solution is too complex to
be found. Because of their abstraction from the brain,
ANNs are good at solving problems that humans are
good at solving but which computers are not. Pattern
recognition and classification are examples of problems
that are well suited for ANN application.
Index Terms: Matlab, source, code, iris, recognition, segmentation, detection, verification, matching, ann, nn, neural, network, networks.
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Figure 1. Neural network example |
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A simple and effective source code for Personal Iris Recognition Using Neural Network. |
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Demo code (protected
P-files) available for performance evaluation. Matlab Image Processing Toolbox, Matlab Neural Network Toolbox and Matlab Signal Processing Toolbox are required. |
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Release |
Date |
Major features |
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1.0 |
2008.12.15 |
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We recommend to check the secure connection to PayPal, in order to avoid any fraud. This donation has to be considered an encouragement to improve the code itself. |
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Personal Iris Recognition Using Neural Network - Click here for
your donation. In order to obtain the source code you
have to pay a little sum of money: 250 EUROS (less
than 350 U.S. Dollars). |
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Once you have done this, please email us luigi.rosa@tiscali.it As soon as possible (in a few days) you will receive our new release of Personal Iris Recognition Using Neural Network. Alternatively, you can bestow using our banking coordinates:
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The authors have no relationship or partnership
with The Mathworks. All the code provided is written in Matlab
language (M-files and/or M-functions), with no dll or other
protected parts of code (P-files or executables). The code was
developed with Matlab 2006a. Matlab Image Processing Toolbox, Matlab Neural Network Toolbox and Matlab Signal Processing Toolbox are required.
The code provided has to be considered "as is" and it is without any kind of warranty. The
authors deny any kind of warranty concerning the code as well
as any kind of responsibility for problems and damages which may
be caused by the use of the code itself including all parts of
the source code.
