  This paper presents an approach to predict the color of skin-with-foundation
based on a no makeup selfie image and a foundation shade image. Our approach
first calibrates the image with the help of the color checker target, and then
trains a supervised-learning model to predict the skin color. In the
calibration stage, We propose to use three different transformation matrices to
map the device dependent RGB response to the reference CIE XYZ space. In so
doing, color correction error can be minimized. We then compute the average
value of the region of interest in the calibrated images, and feed them to the
prediction model. We explored both the linear regression and support vector
regression models. Cross-validation results show that both models can
accurately make the prediction.
