Cargando…

Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space

Previous research has demonstrated the potential to reconstruct human facial skin spectra based on the responses of RGB cameras to achieve high-fidelity color reproduction of human facial skin in various industrial applications. Nonetheless, the level of precision is still expected to improve. Inspi...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Suixian, Xiao, Kaida, Li, Pingqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861444/
https://www.ncbi.nlm.nih.gov/pubmed/36679603
http://dx.doi.org/10.3390/s23020810
_version_ 1784874843058995200
author Li, Suixian
Xiao, Kaida
Li, Pingqi
author_facet Li, Suixian
Xiao, Kaida
Li, Pingqi
author_sort Li, Suixian
collection PubMed
description Previous research has demonstrated the potential to reconstruct human facial skin spectra based on the responses of RGB cameras to achieve high-fidelity color reproduction of human facial skin in various industrial applications. Nonetheless, the level of precision is still expected to improve. Inspired by the asymmetricity of human facial skin color in the CIELab* color space, we propose a practical framework, HPCAPR, for skin facial reflectance reconstruction based on calibrated datasets which reconstruct the facial spectra in subsets derived from clustering techniques in several spectrometric and colorimetric spaces, i.e., the spectral reflectance space, Principal Component (PC) space, CIELab*, and its three 2D subordinate color spaces, La*, Lb*, and ab*. The spectra reconstruction algorithm is optimized by combining state-of-art algorithms and thoroughly scanning the parameters. The results show that the hybrid of PCA and RGB polynomial regression algorithm with 3PCs plus 1st-order polynomial extension gives the best results. The performance can be improved substantially by operating the spectral reconstruction framework within the subset classified in the La* color subspace. Comparing with not conducting the clustering technique, it attains values of 25.2% and 57.1% for the median and maximum errors for the best cluster, respectively; for the worst, the maximum error was reduced by 42.2%.
format Online
Article
Text
id pubmed-9861444
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-98614442023-01-22 Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space Li, Suixian Xiao, Kaida Li, Pingqi Sensors (Basel) Article Previous research has demonstrated the potential to reconstruct human facial skin spectra based on the responses of RGB cameras to achieve high-fidelity color reproduction of human facial skin in various industrial applications. Nonetheless, the level of precision is still expected to improve. Inspired by the asymmetricity of human facial skin color in the CIELab* color space, we propose a practical framework, HPCAPR, for skin facial reflectance reconstruction based on calibrated datasets which reconstruct the facial spectra in subsets derived from clustering techniques in several spectrometric and colorimetric spaces, i.e., the spectral reflectance space, Principal Component (PC) space, CIELab*, and its three 2D subordinate color spaces, La*, Lb*, and ab*. The spectra reconstruction algorithm is optimized by combining state-of-art algorithms and thoroughly scanning the parameters. The results show that the hybrid of PCA and RGB polynomial regression algorithm with 3PCs plus 1st-order polynomial extension gives the best results. The performance can be improved substantially by operating the spectral reconstruction framework within the subset classified in the La* color subspace. Comparing with not conducting the clustering technique, it attains values of 25.2% and 57.1% for the median and maximum errors for the best cluster, respectively; for the worst, the maximum error was reduced by 42.2%. MDPI 2023-01-10 /pmc/articles/PMC9861444/ /pubmed/36679603 http://dx.doi.org/10.3390/s23020810 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Suixian
Xiao, Kaida
Li, Pingqi
Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space
title Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space
title_full Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space
title_fullStr Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space
title_full_unstemmed Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space
title_short Spectra Reconstruction for Human Facial Color from RGB Images via Clusters in 3D Uniform CIELab* and Its Subordinate Color Space
title_sort spectra reconstruction for human facial color from rgb images via clusters in 3d uniform cielab* and its subordinate color space
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9861444/
https://www.ncbi.nlm.nih.gov/pubmed/36679603
http://dx.doi.org/10.3390/s23020810
work_keys_str_mv AT lisuixian spectrareconstructionforhumanfacialcolorfromrgbimagesviaclustersin3duniformcielabanditssubordinatecolorspace
AT xiaokaida spectrareconstructionforhumanfacialcolorfromrgbimagesviaclustersin3duniformcielabanditssubordinatecolorspace
AT lipingqi spectrareconstructionforhumanfacialcolorfromrgbimagesviaclustersin3duniformcielabanditssubordinatecolorspace