Iris pattern extraction using bit planes and standard deviations

Abstract

Iris recognition has been shown to be very accurate for human identification. In this paper, we develop a technique for iris pattern extraction utilizing the least significant bit-plane: the least significant bit of every pixel in the image. Through binary morphology applied to the bit-plane, the pupillary boundary of the iris is determined. The limbic boundary is identified by evaluating the standard deviation of the image intensity along the vertical and horizontal axes. Because our extraction approach restricts localization techniques to evaluating only bit-planes and standard deviations, iris pattern extraction is not dependent on circular edge detection. This allows for an expanded functionality of iris identification technology by no longer requiring a frontal view, which leads to the potential for off-angle iris recognition technology. Initial results show that it is possible to fit a close elliptical approximation to an iris pattern by using only bit-planes and standard deviations for iris localization.