A Color Image Analysis Tool to Help Users Choose a Makeup Foundation Color

TL;DR

This paper introduces a tool that predicts skin color with foundation to assist users in choosing makeup shades, utilizing image calibration and supervised learning for accurate predictions.

Contribution

It proposes a novel calibration method using multiple transformation matrices and compares linear regression and support vector regression for skin color prediction.

Findings

Both models achieved accurate predictions in cross-validation.

Color correction error was minimized through the proposed calibration approach.

The method effectively assists users in selecting foundation shades.

Abstract

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.