Abstract
Skin color provides essential information about an individual's health condition and emotion. Additive manufacturing of human skin has been developed markedly in recent years along with increasing demands for clinical and medical applications. It is therefore critical to achieve precise color reproduction of facial skin and constant color appearance under different illuminations particularly for the application to maxillofacial prostheses. In this study, the color quality of 3D facial prostheses under various illuminations was assessed by measuring human perceptual error, quantified by the color difference metric CIEDE2000, and an index for color constancy. The index was calculated in the same manner as a standard color-constancy index. Thus, in a color space, where the chromaticity coordinates of real skin and artificial skin were located, let a be the distance between real skin and artificial skin colors under a test illuminant and let b be the distance between the real skin color under test and reference illuminants, then the index is 1 – a/b. Perfect constancy corresponds to unity and the greater the error, the lower the index. 3D facial prostheses of three human subjects, one Caucasian and two Chinese, were generated by an additive manufacturing with an elaborated color management from 3D color digital imaging to 3D printing. Colors of the 3D prostheses and subjects' real skin were compared with a spectrophotometer. Mean color difference CIEDE2000 over subjects was approximately 7.2 (a Caucasian 5.7, two Chinese 7.9), slightly larger than the conventional values of acceptable perceptual error (4.0). Despite these differences, color constancy indices between selected CIE standard illuminants (D65, A, F2, F11) ranged over 0.57–0.84, close to values from traditional color-constancy experiments with human observers. The color quality of facial prostheses in modern additive skin manufacturing may be as good perceptually as that of real human skin, even under different scene illuminations.
Meeting abstract presented at VSS 2017