Complete Iris Recognition Preprocessing Framework

The iris recognition preprocessing pipeline is architectured as multiple modular functions: basic image enhancement, Canny operator for edge extraction, Hough transform for circular boundary localization (detecting both inner pupil and outer iris boundaries), and final normalization that unwraps circular iris patterns into rectangular representations. These components collectively enhance preprocessing quality, thereby improving overall iris recognition accuracy. The implementation utilizes OpenCV-based Canny edge detection with dual-threshold hysteresis and Hough circle transform with parameter tuning for optimal boundary detection.

Our package includes sample iris images and a custom MATLAB/Python-based GUI interface. Although the interface requires further polishing, the core preprocessing functionality is robustly implemented. For performance enhancement, we recommend replacing the Hough transform with a least-squares fitting algorithm using circle equation optimization (x-a)²+(y-b)²=r², which can provide sub-pixel boundary localization accuracy. Additional preprocessing techniques like Gaussian filtering for noise reduction and adaptive thresholding for binarization could be integrated to further boost recognition performance through improved image quality and feature extraction.