Precise Skin-Tone and Under-Tone Estimation by Large Photo Set Information Fusion

This paper proposes a novel method for the estimation of a person's skin-tone and under-tone by analyzing a large collection of photos of that person. By excluding badly lit images, and analyzing well-lit skin pixels, it becomes possible to compute an overall skin-tone estimate which is in-line with the person's true skin shade, and based on this, to determine a person's under-tone. Based on a study involving 15,590 user sessions and 104,366 photos, it was found that the proposed methodology can detect the normalized RGB of the person's skin-tone with 2.3% RMSE, or based on the CIE76 color difference measure, obtain an average Delta E color difference of 3.15 in L*a*b* color space.