Iris Segmentation Using Improved Hough Transform

This paper presents an efficient iris segmentation algorithm. It uses an improved circular Hough transform to detect the inner boundary and the integro-differential operator to detect the outer boundary of iris from a given eye image. Search space of the standard circular Hough transform is reduced from three dimensions (center coordinates and radius) to only one dimension, which is the radius. Local gradient information is used to improve time and efficiency of Hough transform. This algorithm has been tested on the publicly available CASIA 3.0 Interval database consisting of 2639 images of 249 subjects. It also provides error categorization for wrong segmentation, as well as a study on parametric influences on error. Parametrized error analysis helps to set parameters intelligently boosting up the segmentation accuracy as high as 99.8% on the database.