Multispectral Iris Fusion for Enhancement, Interoperability, and Cross Wavelength Matching
Proceedings of SPIE(2009)
摘要
Traditionally, only a narrow band of the Near-Infrared (NIR) spectrum (700-900nm) 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.
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关键词
spectrum,visible light,specular reflection,reflectivity,lenses,algorithms,absorbance,iris,specular reflections,iris recognition,near infrared
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