Person Identification through IRIS Recognition

In this paper we propose a new biometric-based Iris feature extraction system. The system automatically acquires the biometric data in numerical format (Iris Images) by using a set of properly located sensors. We are considering camera as a high quality sensor. Iris Images are typically color images that are processed to gray scale images. Then the Feature extraction algorithm is used to detect “IRIS Effective Region (IER)” and then extract features from “IRIS Effective Region (IER)” that are numerical characterization of the underlying biometrics. Later on this work will be helping to identify an individual by comparing the feature obtained from the feature extraction algorithm with the previously stored feature by producing a similarity score. This score will be indicating the degree of similarity between a pair of biometrics data under consideration. Depending on degree of similarity, individual can be identified. Authentication is also a major concern area of this thesis. By considering Biological characteristics of IRIS Pattern we use Statistical Correlation Coefficient for this ‘IRIS Pattern’ recognition where Statistical Estimation Theory can play a big role.