  Face recognition (FR) systems are fast becoming ubiquitous. However,
differential performance among certain demographics was identified in several
widely used FR models. The skin tone of the subject is an important factor in
addressing the differential performance. Previous work has used modeling
methods to propose skin tone measures of subjects across different
illuminations or utilized subjective labels of skin color and demographic
information. However, such models heavily rely on consistent background and
lighting for calibration, or utilize labeled datasets, which are time-consuming
to generate or are unavailable. In this work, we have developed a novel and
data-driven skin color measure capable of accurately representing subjects'
skin tone from a single image, without requiring a consistent background or
illumination. Our measure leverages the dichromatic reflection model in RGB
space to decompose skin patches into diffuse and specular bases.
