Applications of Capacitive Imaging in Human Skin Texture and Hair Analysis
Journal article
Bontozoglou, C. and Xiao, P. (2019). Applications of Capacitive Imaging in Human Skin Texture and Hair Analysis . Applied Sciences. 10 (1), p. 256. https://doi.org//10.3390/app10010256
Authors | Bontozoglou, C. and Xiao, P. |
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Abstract | This article focuses on the extraction of information from human skin and scalp hair for evaluation of a subject’s condition in the cosmetic and pharmaceutical industries. It uses capacitive images from existing hand-held research equipment and it applies image processing algorithms to expand their possible applications. The literature review introduces the readers into the field of skin research, and it highlights pieces of information that can be extracted by in vivo skin and ex vivo hair measurements. Then, the selected scientific equipment is presented, and Maxwell-based electrostatic simulations are employed to evaluate the measurement apparatus. Image analysis algorithms are suggested for (a) the detection of polygons on the human skin texture, (b) the estimation of wrinkles length and (c) the observation of hair water sorption capabilities by capacitive imaging systems. Finally, experiments are conducted to evaluate the performance of the presented algorithms and the results are compared with the literature. The results indicate that capacitive imaging systems can be used for skin age classification, detection and tracking of skin artifacts (e.g., wrinkles, moles or scars) and calculation of water content in hair samples. |
Keywords | texture; skin microrelief; water sorption; aging; hair |
Year | 2019 |
Journal | Applied Sciences |
Journal citation | 10 (1), p. 256 |
Publisher | MDPI |
Digital Object Identifier (DOI) | https://doi.org//10.3390/app10010256 |
Web address (URL) | https://doi.org/10.3390/app10010256 |
Publication dates | |
29 Dec 2019 | |
Publication process dates | |
Accepted | 24 Dec 2019 |
Deposited | 23 Jan 2020 |
Publisher's version | License File Access Level Open |
https://openresearch.lsbu.ac.uk/item/88x48
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