A Color Image Analysis Tool to Help Users Choose a Makeup Foundation Color
This addresses a practical problem for consumers choosing makeup foundation colors, but appears to be an incremental application of existing methods to a new domain.
The paper tackles the problem of predicting how makeup foundation colors will appear on skin by developing a supervised learning model that uses calibrated images of bare skin and foundation shades. Results show both linear regression and support vector regression models can accurately predict skin-with-foundation colors, though no specific accuracy numbers are provided.
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.