Face Recognition Using Neural Network - Seminar Reports|PPT|PDF|DOC|Presentation

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.