Statistical Characterization of Spectral Reflectances in Spectral Imaging of Human Portraiture

Spectral reflectances of various parts of human faces from various ethnic races were collected from experiments on spectral imaging of human portraits. Principal components analysis (PCA) was applied to those spectral reflectances not only from different races, but also from different face parts. The first three principal components can explain about 99.8% of cumulative contribution of variance of spectral reflectances for each race and each face part, and all races as well. Color differences of spectral reconstruction either for different races or for different face parts based on different sets of principal components were estimated. The results indicate that, when using 3 basis functions and under D50 illumination, the basis functions based only on spectra of Pacific-Asian will provide best overall color reproduction. However, from spectral matching point of view, three basis functions based on all spectra will provide the best spectral reproduction with minimum overall mean value of indices of metamerism. More analyses and comparison were applied to spectral reflectances of human facial skin from different sources. Those results provide practical suggestions for imaging or spectral imaging system design, especially imaging systems for human portraiture.