Iris Recognition Using Gaussian Pyramid Compression and Modified Distance Measures

Iris recognition is one of important biometric recognition approach in a human identification is becoming very active topic in research and practical application. In this paper, Gaussian pyramid compression technique is used to compress the eye image and this compressed eye is used for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from the compressed eye image and after normalization and enhancement it is represented by a data set. With Gaussian pyramid compression improved matching performance is observed down to 0.25 bits/pixel (bpp), attributed to noise reduction without a significant loss of texture. To ensure that, the irismatching algorithms are not degraded by image compression. Hamming distance, Modified Normalized Euclidean Distance and Modified Average Euclidean Distance methods are evaluated using MMU iris image database [8] and achieved high accuracy. Experimental results demonstrate that the proposed methods can be used for human identification in an efficient manner.