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.

Performance Evaluation of New Feature based on Ordinal Pattern Analysis for Iris Biometric Recognition

“…Recently, new approaches based on the quantification of image irregularity using nonlinear measures have proven to be efficient in the characterization of fine details of two-dimensional images [3]. Successful applications of such methods in the characterization and analysis of small fine textural details of images in various technological as well as biomedical fields have been reported in the literature [4]. Efficient characterization of the iris patterns is an ever-demanding research topic due to the fact that its success and failure will lead to extremely sensitive outcomes related to national and international security and related contexts, as well as the reliability of general commercial applications.…”