Localization Process in Iris Image Processing

In iris image processing, localization represents a critical procedural step. To achieve precise iris region identification, we implement a methodology combining binarization and Hough transform techniques. Initially, the image undergoes binarization processing through thresholding operations (typically using Otsu's method or adaptive thresholding) to distinguish iris regions from background elements. Subsequently, the Hough transform method is employed, which operates by detecting circular patterns through accumulator voting mechanisms in the binarized image space. This localization approach effectively extracts iris boundaries with demonstrated robustness against noise and illumination variations, providing an accurate foundation for subsequent image processing stages and feature extraction workflows. The implementation typically involves OpenCV functions like cv2.HoughCircles() for circular detection and cv2.threshold() for image binarization, with parameter optimization for pupil and limbic boundary detection.