Assessing gloss perception of human facial skin across subject

Jing Wang, Thrasyvoulos N Pappas, Carla Kuesten, Gopa Majmudar, Jim Mayne

Research output: Contribution to journalConference article

Abstract

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.

Original languageEnglish (US)
JournalIS and T International Symposium on Electronic Imaging Science and Technology
DOIs
StatePublished - Jan 1 2018
Event2018 Human Vision and Electronic Imaging, HVEI 2018 - Burlingame, United States
Duration: Jan 28 2018Feb 1 2018

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Skin
Statistics
Photography
statistics
scoring
performance tests
photography
learning
margins
evaluation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Human-Computer Interaction
  • Software
  • Electrical and Electronic Engineering
  • Atomic and Molecular Physics, and Optics

Cite this

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title = "Assessing gloss perception of human facial skin across subject",
abstract = "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.",
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Assessing gloss perception of human facial skin across subject. / Wang, Jing; Pappas, Thrasyvoulos N; Kuesten, Carla; Majmudar, Gopa; Mayne, Jim.

In: IS and T International Symposium on Electronic Imaging Science and Technology, 01.01.2018.

Research output: Contribution to journalConference article

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