Neural Network Technology

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.