Human Iris biometric Authentication Based on Statistical Linear Correlation Coefficient

In this paper we proposed a method of using Statistical Linear Correlation Coefficient for Human Iris Authentication. In this work all images were taken from MMU1 Iris database. Each of them contributes 5 iris images for each eye. Images are 100 x 100 24- bit Bitmap (.bmp), each occupying 32, 768 bytes on hard drive. Here, 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. A correlation greater than 0.8 is generally described as strong, whereas a correlation less than 0.5 is generally described as weak. These values can vary based upon the “type” of data being examined. The derived equations are used in algorithm for calculation of correlation coefficient. The Iris recognition performance is evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR).