Colour constancy algorithms range from image statisticsbased pixel intensity manipulation to gamut-mapping methods, and are generally independent of specific image contents. In previous work, we have demonstrated that natural polychromatic surfaces possess distinct chromatic signatures in conecontrast space that may be exploited for colour constancy, and that in human vision, colour constancy is improved for such objects. Here we set out to use the specific, recognisable, and ubiquitous content of human skin in colour images to drive a gamut mapping method for colour constancy. We characterise variations in the chromaticity gamut of varying types of, pre recognised, human skin (male, female; Caucasian, African, Asian) under varying illumination. We use a custom-built LED illuminator to produce daylight metamers, and a spectroradiometrically calibrated hyperspectral camera (Specim V10E) to acquire images and create a novel hyperspectral skin image database. We demonstrate that human skin gamuts in conecontrast space are characterised by a set of features that can be used to differentiate between similar illuminations, whose estimate can then be used to colour correct an image.
['Stuart Crichton', 'Jonas Pichat', 'Michal Mackiewicz', 'Gui Yun Tian', 'Anya Hurlbert']
Skin chromaticity gamuts for illumination recovery