Multispectral Iris Fusion and Cross-Spectrum Matching

Abstract

Traditionally, only a narrow band of the Near-Infrared (NIR) spectrum (700–900 nm) is utilized for iris recognition since this alleviates any physical discomfort from illumination, reduces specular reflections and increases the amount of texture captured for some iris colors. However, previous research has shown that matching performance is not invariant to iris color and can be improved by imaging outside of the NIR spectrum. Building on this research, we demonstrate that iris texture increases with the frequency of the illumination for lighter colored sections of the iris and decreases for darker sections. Using registered visible light and NIR iris images captured using a single-lens multispectral camera, we illustrate how physiological properties of the iris (e.g., the amount and distribution of melanin) impact the transmission, absorbance, and reflectance of different portions of the electromagnetic spectrum and consequently affect the quality of the imaged iris texture. We introduce a novel iris code, Multispectral Enhanced irisCode (MEC), which uses pixel-level fusion algorithms to exploit texture variations elicited by illuminating the iris at different frequencies to improve iris matcher performance and reduce Failure-To-Enroll (FTE) rates. Finally, we present a model for approximating an NIR iris image using features derived from the color and structure of a visible light iris image. The simulated NIR images generated by this model are designed to improve the interoperability between legacy NIR iris images and those acquired under visible light by enabling cross wavelength matching of NIR and visible light iris images.