Multispectral Skin Color Modelling
Embargo Date
Related Collections
Degree type
Discipline
Subject
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Contributor
Abstract
The automated detection of humans in computer vision as well as the realistic rendering of people in computer graphics necessitates improved modeling of the human skin color. We describe the acquisition and modeling of skin reflectance data densely sampled over the entire visible spectrum. The data collected through a spectrograph allows us to explain skin color (and its variations) and to discriminate between human skin and dyes designed to mimic human skin. We study the approximation of these data using several sets of basis functions. Our study shows that skin reflectance data can best be approximated by a linear combination of Gaussians or their first derivatives. This result has a significant practical impact on optical acquisition devices: the entire visible spectrum of skin reflectance can now be captured with a few filters of optimally chosen central wavelengths and bandwidth.
Advisor
Date of presentation
Conference name
Conference dates
Conference location
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
Comments
Copyright 2001 IEEE. Reprinted from Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Volume 2, pages II-635 - II-642. Publisher URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=21365&page=6 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
Copyright 2001 IEEE. Reprinted from Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Volume 2, pages II-635 - II-642. Publisher URL: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isNumber=21365&page=6 This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.