Iris Localization Using Hough Transform in MATLAB with Code Implementation

In this article, we explore how to implement iris localization using Hough transform in MATLAB. The Hough transform is a fundamental image processing technique that detects geometric features like lines and curves in images. For iris localization, we specifically utilize the Circular Hough Transform to identify circular patterns in iris images, enabling accurate iris boundary detection. The implementation involves several key steps: First, we preprocess the eye image using edge detection algorithms like Canny or Sobel to highlight iris boundaries. Then we apply MATLAB's imfindcircles function or implement a custom Hough circle detection algorithm that accumulates votes in a 3D parameter space (x-center, y-center, radius). The function hough, houghpeaks, and houghlines can be adapted for circle detection by modifying the parameterization. The algorithm works by transforming edge points from Cartesian coordinates to Hough space, where circles are represented by their center coordinates and radii. Peaks in the Hough accumulator matrix correspond to probable circles in the original image. We discuss parameter optimization for handling varying iris sizes and contrast conditions, including sensitivity thresholds and radius range specifications. This article details both the theoretical principles of Hough transform and practical MATLAB implementation steps for iris localization, complete with code snippets demonstrating parameter tuning and result visualization using imshow and viscircles functions. We hope this resource provides valuable insights for those studying image processing techniques and biometric identification systems.