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Face Recognition Using Neural Network


Abstract of Face Recognition Using Neural Network

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.

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.
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 :

1. It requires no physical inetraction on behalf of the user.
2. It is accurate and allows for high enrolment and verification rates.
3. It does not require an expert to interpret the comparison result.
4. It can use your existing hardware infrastructure, existing camaras and image capture devices will work with no problems.
5. 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.


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.








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


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