Texture Entropy-Based Classification for Iris Recognition Systems

Abstract

In recent years, iris recognition is one of the most widely used techniques for person identification. Automatic iris identification implies a comparison of query iris image with iris entries in a large database to determine the identity of the person. In this paper, we propose a straightforward and effective algorithm for the classification of irises into several categories according to the iris texture characteristics. The goal of the classification is to identify and retrieve a smaller subset of the large database and to narrow down the search space. In this way, the response time of the iris recognition system could be significantly improved. We analyzed several cases for dividing the whole database (we used UPOL, CASIA, and UBIRIS databases) into up to eight subsets and calculated the time savings. The simulation results illustrate the potential of the proposed classification method for large-scale iris databases.