Iris Recognition Algorithm

The iris recognition algorithm is a highly useful and sophisticated biometric technique that comprises three key stages: iris localization, normalization, and encoding. These sequential processing steps are essential for achieving accurate and reliable iris recognition systems.

The localization phase primarily utilizes advanced image processing techniques to identify the precise position and boundaries of the iris within the eye image. Common implementation approaches involve using circular edge detection algorithms (such as Hough transform) to locate the pupillary and limbic boundaries, often employing functions like Canny edge detection followed by circular fitting methods.

The normalization stage standardizes the extracted iris region into a fixed-dimensional representation, typically converting the annular iris region into a rectangular texture pattern using techniques like Daugman's rubber sheet model. This process eliminates variations caused by different image acquisition conditions and ensures consistency for subsequent comparison operations.

The encoding phase transforms the normalized iris texture into a unique mathematical feature vector or iris code. This is commonly implemented using 2D Gabor wavelets or other filter banks to capture the distinctive texture patterns, generating a compact binary code that facilitates efficient comparison with stored iris templates through Hamming distance calculations.

Iris recognition algorithms have extensive applications across various security domains, including but not limited to identity authentication, secure access control systems, criminal investigations, and border security implementations.