Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps:1) A set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) Using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. A fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. The process of data collection acquired through the ocean of cloud computing, analyzed in cluster analysis and applied with the various data mining techniques to extract the needed data. As many as 15 types of identifications schemes are used in general elections nowadays. So the rate of interchange is high. But, this can be avoided by our new innovative possible technological trend of implementing "IRIS SCANNING MECHANISM". Iris is the colored part of the eye that consists of a muscular diaphragm surrounding the pupil and regulating the light entering the eye by expanding and contracting the pupil. As the ratio of people having same iris structure is 10^78 it have adopted this mechanism. Thus providing biometric security, it signs to bring secured democracy in our nation.
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Biometric security, Data mining, Cloud Computing, IRIS Scanning Mechanism, Fingerprint, GAIC Algorithm