Iris Region Extraction from Eye Images with Dual-Stage Localization Algorithm

Iris region extraction from eye images is a biometric recognition method based on ocular imagery. This technique enables individual identification and authentication by analyzing the iris region within eye images. The extraction process employs a coarse-to-fine dual localization approach: initially detecting the approximate iris position through circular edge detection algorithms (typically using Hough transform or integro-differential operators), followed by refined localization using precise boundary optimization techniques. Key implementation steps include image preprocessing (noise reduction and contrast enhancement), pupillary boundary detection, and limbus boundary identification. By extracting the iris region, distinctive biometric features can be obtained for subsequent pattern matching and identity verification through feature encoding and comparison algorithms (such as Gabor wavelet filtering or ordinal measures). Iris region extraction holds significant application value in biometric authentication systems, with widespread implementation potential in security certification, access control systems, and personal identity verification domains. The methodology typically involves OpenCV or MATLAB implementations for image processing operations and machine learning libraries for feature matching.