Efficient Iris Recognition by Characterizing Key Local Variations

TLDR

Iris identification relies on randomly distributed features that make it highly reliable yet difficult to represent in images. This study proposes an efficient iris recognition algorithm that characterizes key local variations. The method extracts local sharp variation points by converting the iris image into one‑dimensional intensity signals, applying wavelet analysis to record their positions, and then matching these position sequences using a fast XOR operation. Experiments on 2,255 iris images demonstrate that the algorithm achieves encouraging accuracy comparable to the best existing iris recognition methods.

Abstract

Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.