Nonideal Iris Recognition Using Level Set Approach and Coalitional Game Theory

摘要

This paper presents an efficient algorithm for iris recognition using the level set approach and the coalitional game theory. To segment the inner boundary from a nonideal iris image, we apply a level set based curve evolution approach using the edge stopping function, and to detect the outer boundary, we employ the curve evolution approach using the regularized Mumford-Shah segmentation model with an energy minimization approach. An iterative algorithm, called the Contribution-Selection Algorithm (CSA), in the context of coalitional game theory is used to select the optimal features subset without compromising the accuracy. The verification performance of the proposed scheme is validated using the UBIRIS Version 2, the ICE 2005, and the WVU datasets.