311 Characterizing and Modeling Complexion spots, and shine in determining people’s preferences for ideal complexion, and investigated their combined effects via a multivariate regression model, which provides a key tool for us to quantitatively describe ideal complexion. By analyzing facial images from a large female Chinese population (N =480), we show that this newly defined skin parameter of ideal complexion is normally distributed in the studied population with the vast majority exhibiting ICS values between 10% and 90% quantile (3.93–7.40) while the rest tailed off in the high and low ends (90% and 10% quantiles). The visual differences in high and low levels of ICS shown in Figure 10 agreed well with the general perception of Asians toward ideal complexion that is, skins with fair and even skin tone, fine texture, and free of pigmentary blemishes are preferred. This study helped provide data for adequate understanding of how ideal complexion is perceived by Chinese women as well as for objective quantification of the consumer-perceivable skin properties. The results of treatment efficacy evaluation provided data to demonstrate that a product claim of obtaining ideal complexion could be substantiated and the consumer preference likelihood could be quantitatively estimated based on the changes of the objectively measured skin properties. Regarding modeling complex skin properties, one general question that often arises is whether the multivariable model has added benefit in predicting a phenomenon when compared to each of the individual parameters. The added benefit was evident in this study. As shown in Figure 12, individually, the highest correlation coefficient was 0.5318, which was much less than that of the model, 0.9866, as shown in Figure 6. One thing worth noticing, however, is the behavior of dINT. While it correlated poorly with Bradley–Terry P individually (r =0.1268), significant contribution to Bradley–Terry P was shown in the multivariable model, owing to its interactions with other parameters to minimize residuals (Figure 7B). SVS showed a similar behavior but in an opposite direction—individually it correlated well with Bradley–Terry P (r =0.5008), but its contribution was not statistically significant in multiple regression analysis (Figure 7E). CONCLUSIONS From this study we concluded that skin complexion, a subjective and consumer-perceivable property of skin, could be characterized, quantified, and predicted with a linear model by using objectively measured basic visual attributes of facial skin. Use of paired comparison coupled with logit function, image analysis of basic skin parameters, and the Bradley–Terry Figure 12. Multivariate analysis showing correlations of individual skin attributes with the panel preference rank (Bradley–Terry P). Table on top: correlation coefficient of Bradley–Terry P and DRR, SVS, ITA°, etc. Charts on bottom: scatterplots with density ellipses and regression line showing each correlation.

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