Implementation of the Hough Transform for Iris Detection and Segmentation

Abstract

The iris is used as a reference for the study of unique biometric marks in people. The analysis of how to extract the iris characteristic information represents a fundamental challenge in image analysis, due to the implications it presents: detection of relevant information, data coding schemes, etc. For this reason, in the search for extraction of useful and characteristic information, approximations have been proposed for its analysis. In this article, it is presented a scheme to extract the relevant information based on the Hough transform. This transform helps to find primitive geometries in the irises, which are used to characterize each one of these. The results of the implementation of the algorithm of the Hough transform applied to the location and segmentation of the iris by means of its circumference are presented in the paper. Two public databases of iris images were used: UBIRIS V2 and CASIA-IrisV4, which were acquired under the same conditions and controlled environments. In the pre-processing stage the edges are found from the noise elimination in the image through the Canny detector. Subsequently, to the images of the detected edges, the Hough transform is applied to the disposition of the geometries detected.

Iris recognition method based on segmentation

“…When identifying, only need to compare the iris characteristic data of the person to be tested to identify the individual [13]. Determining the correct segmentation methods of the iris is the most important stage in the verification system, including finding the limbic boundaries of the iris and the pupil of the eye, whether there is an effect of eyelids and shadows, and not exaggerating the centrality that reduces the effectiveness of the iris recognition system [14]. The sensors capture the iris segmentation images and are determined inside the image by the preprocessing unit, and the iris portion is extracted to process it.…”