Iris Recognition MATLAB Source Code Utilizing Hough Transform

The MATLAB source code for iris recognition employs the Hough transform as a key processing component. Iris recognition represents a biometric identification technology that authenticates individuals by analyzing and comparing iris patterns from eye images. During the iris recognition process, the Hough transform serves as a fundamental mathematical transformation method that converts image representations between different domains, facilitating subsequent image processing and analysis operations. The MATLAB implementation utilizes the Hough transform primarily for iris boundary detection and feature extraction. In the code structure, this typically involves: - Implementing circular Hough transform for pupil and iris boundary localization - Applying linear Hough transform for eyelid and eyelash detection - Converting Cartesian coordinate system image data to polar coordinates for normalized representation Through the Hough transform implementation, the source code achieves robust feature extraction and pattern matching capabilities. The algorithm enhancement includes: - Edge detection preprocessing using operators like Canny or Sobel - Hough space accumulation for parameter voting - Peak detection in Hough space to identify circular patterns - Coordinate transformation for iris normalization This implementation significantly improves the accuracy and reliability of the iris recognition system by providing stable feature representation and reducing sensitivity to illumination variations and positional changes. The code structure typically separates the Hough transform module into dedicated functions for boundary detection, feature encoding, and matching algorithms.