The well-known simultaneous contrast effect describes how surrounding surfaces influence lightness perception. Similar contextual effects are ubiquitous in the lightness literature. Contextual effects in gloss perception however, have not yet been studied intensively. Here, we describe two distinct studies that investigate the role of spatial interactions between different glossy materials. In a first study we produced real surfaces that contain two different materials and compared perceived gloss in two conditions: in isolation and in context with a second material. Our results provide strong evidence that the context largely influences perceived gloss. Gloss ratings of identical materials differed depending on the presentation mode. In a second study we wished to quantify the strength of these contextual effects using Maximum likelihood conjoint measurement. We used glossy versions of the simultaneous contrast display and again found strong influences of albedo and gloss of the surroundings on perceived gloss and lightness. Both studies hint towards a profound influence of the context on perceived gloss. Investigating spatial interactions between materials within a scene has largely been studied in the lightness literature but only received moderate attention in the gloss literature. Our results provide confirmatory evidence that perceived gloss is shaped by other materials in the scene.
We propose novel techniques for the evaluation of perceived facial gloss across subjects with varying surface reflections. Given a database of facial skin images from multiple subjects, ordered according to perceived gloss within each subject, we propose a head-tail (least and most glossy image of each subject) selective comparison approach for ordering the entire database. We conducted a two-alternative forced-choice empirical study to compare the facial gloss across subjects within each group. Using the gloss scores of selected candidates and the gloss range of a reference subject, we fit each within-subject gloss range to a global gloss range and quantized the scores into distinct gloss levels. We then conducted another empirical study to validate the quantized gloss levels. The results show that in 90% of the cases, the levels are consistent with human judgments. Based on the database with quantized gloss levels, we develop a max-margin learning model for facial skin gloss estimation. The model relies on gloss related statistics extracted from surface and subsurface reflection images obtained using multimodal photography. The predicted gloss level is decided by the nearest neighbors using the learned scoring function. Performance tests demonstrate that the best performance, with 82% accuracy, is obtained when we combine local statistics from both surface and subsurface reflections.