Improving Iris-Based Personal Identification Using Maximum Rectangular Region Detection

Iris recognition is proving to be one of the most reliable biometric traits for personal identification. In fact, iris patterns have stable, invariant and distinctive features for personal identification. In this paper, we propose a new algorithm that detects the largest non-occluded rectangular part of the iris as region of interest (ROI). Thereafter, a cumulative-sum-based grey change analysis algorithm is applied to the ROI to extract features for recognition. This method could possibly be utilized for partial iris recognition since it relaxes the requirement of using the whole part of the iris to produce an iris template. Preliminary experimental results carried on a CASIA iris database, show that the approach is promisingly effective and efficient.