Abstract of Face Recognition Using Neural

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
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.
Neural network is a sequence of neuron layers. A
neuron is a building block of a neural net. It is very loosely based on the
brain’s nerve cell. Neurons will receive inputs via weighted links from other
neurons. This inputs will be processed according to the neurons activation
function. Signals are then passed on to other neurons.
In a more practical way, neural networks are made
up of interconnected processing elements called units which are equivalent to
the brains counterpart ,the neurons.
Neural network can be considered as an artificial
system that could perform “intelligent” tasks similar to those
performed by the human brain. 
Neural networks resemble the human brain in the
following ways:

1. A neural network acquires knowledge through learning.

2. A neural network’s knowledge is stored within inter-neuron connection
strengths known as synaptic weights.
3. Neural networks modify own topology just as neurons in the brain can die and
new synaptic connections grow.

Why we choose face recognition over other biometric?

There are a number reasons to choose
face recognition. This includes the following :

It requires no physical inetraction on behalf of the
 It is accurate
and allows for high enrolment and verification rates.
 It does not
require an expert to interpret the comparison result.
 It can use your
existing hardware infrastructure, existing camaras and image capture devices
will work with no problems.
 It is the only
biometric that allow you to perform passive identification in a one to many
environment (eg: identifying a terrorist in a busy Airport terminal.

The face is an important part of who you are and how people identify you.
Except in the case of identical twins, the face is arguably a person’s most
unique physical characteristics. While humans have the innate ability to
recognize and distinguish different faces for millions of years , computers are
just now catching up. For face recognition there are two types of comparisons
.the first is verification. This is where the system compares the given
individual with who that individual says they are and gives a yes or no
decision. The second is identification. This is where the system compares the
given individual to all the other individuals in the database and gives a
ranked list of matches. All identification or authentication technologies
operate using the following four stages:

1. capture: a physical or behavioural sample is captured by the
system during enrollment and also in identification or verification process.
2. Extraction: unique data is extracted from the sample and a
template is created.
3. Comparison: the
template is then compared with a new sample.
4. Match/non match : the system decides if the features
extracted from the new sample are a match or a non match.
Face recognition starts with a picture,
attempting to find a person in the image. This can be accomplished using
several methods including movement, skin tones, or blurred human shapes. The
face recognition system locates the head and finally the eyes of the
individual. A matrix is then developed based on the characteristics of the
individual’s face. The method of defining the matrix varies according to the
algorithm (the mathematical process used by the computer to perform the
comparison). This matrix is then compared to matrices that are in a database
and a similarity score is generated for each comparison.
Artificial intelligence is used to simulate human
interpretation of faces. In order to increase the accuracy and adaptability,
some kind of machine learning has to be implemented.
There are essentially two methods of capture. One
is video imaging and the other is thermal imaging. Video imaging is more common
as standard video cameras can be used. The precise position and the angle of
the head and the surrounding lighting conditions may affect the system
performance. The complete facial image is usually captured and a number of
points on the face can then be mapped, position of the eyes, mouth and the
nostrils as a example. More advanced technologies make 3-D map of the face
which multiplies the possible measurements that can be made. 
Thermal imaging has better accuracy as it uses
facial temperature variations caused by vein structure as the distinguishing
traits. As the heat pattern is emitted from the face itself without source of
external radiation these systems can capture images despite the lighting
condition, even in the dark. The drawback is high cost. They are more expensive
than standard video cameras. 
Face recognition technologies have been
associated generally with very costly top secure applications. Today the core
technologies have evolved and the cost of equipments is going down dramatically
due to the intergration and the increasing processing power. Certain
application of face recognition technology are now cost effective, reliable and
highly accurate. As a result there are no technological or financial barriers for
stepping from the pilot project to widespread deployment.

1. ELECTRONICS FOR YOU- Part 1 April 2001 & Part 2 May 2001

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