Iris and Pupil Segmentation Using Binarization Method

This iris and pupil segmentation algorithm was independently developed during my thesis research, employing binarization methods with supporting visual examples. I welcome potential research collaborations. In this paper, I provide a detailed explanation of my custom-developed iris and pupil segmentation methodology. The approach utilizes binarization techniques that convert grayscale images into binary format (black and white pixels) to accurately delineate iris and pupil boundaries. The implementation typically involves thresholding operations where pixel intensities below a certain threshold are classified as pupil/iris regions, while higher intensities represent other ocular features. Accompanying sample images demonstrate the algorithm's effectiveness in different lighting conditions and image qualities. I enthusiastically invite researchers interested in biometric identification and image processing to collaborate on this project. The code implementation includes key functions for image preprocessing, adaptive threshold calculation, and morphological operations to enhance segmentation accuracy. If you have expertise in computer vision, pattern recognition, or related fields, and wish to contribute ideas, suggestions, or cooperative research, please feel free to contact me. Through collaborative efforts and knowledge exchange, we can advance this technology and contribute significantly to the development of more robust iris recognition systems.