Abstract

In most iris recognition systems the texture of the iris image is either the result of the interaction between the iris and Near Infrared (NIR) light, or between the iris pigmentation and visible-light. The iris, however, is a three-dimensional organ, and the information contained on its relief is not being exploited completely. In this article, we present an image acquisition method that enhances viewing the structural information of the iris. Our method consists of adding lateral illumination to the visible light frontal illumination to capture the structural information of the muscle fibers of the iris on the resulting image. These resulting images contain highly textured patterns of the iris. To test our method, we collected a database of 1,920 iris images using both a conventional NIR device, and a custom-made device that illuminates the eye in lateral and frontal angles with visible-light (LFVL). Then, we compared the iris recognition performance of both devices by means of a Hamming distance distribution analysis among the corresponding binary iris codes. The ROC curves show that our method produced more separable distributions than those of the NIR device, and much better distribution than using frontal visible-light alone. Eliminating errors produced by images captured with different iris dilation (13 cases), the NIR produced inter-class and intra-class distributions that are completely separable as in the case of LFVL. This acquisition method could also be useful for 3D iris scanning.