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Copyright of International Journal of Cosmetic Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We propose objective, image-based techniques for quantitative evaluation of facial skin gloss that is consistent with human judgments. We use polarization photography to obtain separate images of surface and subsurface reflections, and rely on psychophysical studies to uncover and separate the influence of the two components on skin gloss perception. We capture images of facial skin at two levels, macro-scale (whole face) and meso-scale (skin patch), before and after cleansing. To generate a broad range of skin appearances for each subject, we apply photometric image transformations to the surface and subsurface reflection images. We then use linear regression to link statistics of the surface and subsurface reflections to the perceived gloss obtained in our empirical studies. The focus of this paper is on within-subject gloss perception, that is, on visual differences among images of the same subject. Our analysis shows that the contrast of the surface reflection has a strong positive influence on skin gloss perception, while the darkness of the subsurface reflection (skin tone) has a weaker positive effect on perceived gloss. We show that a regression model based on the concatenation of statistics from the two reflection images can successfully predict relative gloss differences. [ABSTRACT FROM AUTHOR]

Measuring and analyzing time-intensity ( T- I) of sensory attributes is an important subject of sensory analysis. This paper proposes using functional data analysis ( FDA), an emerging field in statistical research, as a new strategy and framework for sensory T- I data analysis. This paper discusses and illustrates how to apply the well-developed FDA techniques and use the available R package 'fda' to analyze sensory T- I data. The analyses include data smoothing, descriptions of functional data, functional regression analysis, functional analysis of variance and permutation tests of functional hypotheses (functional F-tests and t-tests). Some conclusions about applications of FDA to sensory T- I data are reached. Practical Applications The currently used statistical analysis for sensory T- I data is not well developed. There are some limitations of using univariate and multivariate data analyses for sensory T- I data. FDA is a promising alternative. This paper shows some advantages using FDA and the R package 'fda' for the analysis of sensory T- I data. It is expected that the introduction and application of FDA in the sensory field will dramatically improve data analysis for T- I evaluation of sensory attributes. [ABSTRACT FROM AUTHOR]