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 FIGURE 1: Example of Iris An automated use of iris recognition as a means of authentication has been originally proposed by Flom and Safir. Daugman has proposed an operational iris recognition system in 1994 [2]. Since then, iris biometric is evolved as a standard reference model for verification. Iris recognition technology offers the highest accuracy in identifying individuals of any method available. 

 This is because no two irises are alike - not between identical twins, or even between the left and right eye of the same person. Irises are also stable; unlike other identifying characteristics that can change with age; the pattern of one's iris is fully formed by ten months of age and remains the same for the duration of their lifetime [3]. Iris recognition technology is also accurate because it uses more than 240 points of reference, in iris pattern, as a basis for a match.

Some studies [4] have showed that the performance of any single trait verification system can be improved by unimodal (or bimodal) fusion. i.e. the combination of various verification strategies applied on the same input data. Even grater verification performance improvement can be expected trough the use of multiple biometric characteristics if we assume statistical independence between them. Work related to multimodal fusion approach is given in [5] [6] [7]. Multialgorithmic biometric systems t a k e a single sample from a single sensor and process that sample with two or more different algorithms. The technique could be applied to any modality. Maximum benefit would be derived from algorithms that are based on distinctly different and independent principles. An intelligent fusion algorithm combines the score to improve the iris recognition performance and reduce the false rejection rate [8] [9]. 

## 1.1 Proposed Approach

Our basic study of the Daugman's mathematical algorithms for iris processing, derived from the information found in the open literature, led us to suggest a few possible methods [2]. Iris recognition technology works by combining computer vision, pattern recognition, and optics. First, a black-and-white video camera zooms in on the iris and records a sharp image of it. The iris is lit by a low-level light to aid the camera in focusing. A frame from this video is then digitized into a 512 byte file and stored on a computer database. There are three main stages in iris recognition system Image preprocessing Feature Extraction Template Matching In this paper we use hough transform for localization and segmentation of iris image. Dougmans rubber sheet model for iris normalization. For feature extraction we are using three distinct algorithms i.e. zero-crossing 1 D wavelet Euler No., and genetic algorithm. The score basefusion 