The information
age is quickly revolutionizing the way transactions are completed. Everyday actions are
increasingly being handled electronically, instead of with pencil and paper or
face to face. This growth in electronic transactions has resulted in a greater
demand for fast and accurate user identification and authentication.
Access codes for buildings, banks accounts
and computer systems often use PIN's for
identification and security clearances.
Using the proper PIN
gains access, but the user of the PIN is not verified. When credit and ATM cards are lost or stolen,
an unauthorized user can often come
up with the
correct personal codes.
Despite warning , many people
continue to choose
easily guessed PIN's
and passwords: birthdays, phone numbers and social security
numbers. Recent cases of identity theft have
hightened the nee
for methods to
prove that someone
is truly who
he/she claims to be.
Face recognition
technology may solve this problem since a face is undeniably connected to its owner expect in the case of
identical twins. Its nontransferable. The
system can then
compare scans to
records stored in a
central or local database or even on a smart card.
A neural network is a
powerful data modeling tool that is able to
capture and represent complex input/output relationships . In the
broader sense, a neural network is a collection of mathematical models that
emulate some of the observed properties of biological nervous systems and draw
on the analogies of adaptive biological learning. It is composed of a large
number of highly interconnected processing elements that are analogous to
neurons and are tied together with weighted connections that are analogous to
synapses.
To be more
clear, let us study the model of a neural network with the help of
figure.1. The most common neural network model is the multilayer perceptron
(MLP). It is composed of hierarchical layers of neurons arranged so
that information flows from the input layer to the output layer of the network.
The goal of this type of network is to create a model that correctly maps the
input to the output using historical data so that the model can then be used to
produce the output when the desired output is unknown.
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