Browse > Article
http://dx.doi.org/10.3745/KTSDE.2022.11.1.41

Analysis of Skin Color Pigments from Camera RGB Signal Using Skin Pigment Absorption Spectrum  

Kim, Jeong Yeop (영산대학교 성심교양대학)
Publication Information
KIPS Transactions on Software and Data Engineering / v.11, no.1, 2022 , pp. 41-50 More about this Journal
Abstract
In this paper, a method to directly calculate the major elements of skin color such as melanin and hemoglobin from the RGB signal of the camera is proposed. The main elements of skin color typically measure spectral reflectance using specific equipment, and reconfigure the values at some wavelengths of the measured light. The values calculated by this method include such things as melanin index and erythema index, and require special equipment such as a spectral reflectance measuring device or a multi-spectral camera. It is difficult to find a direct calculation method for such component elements from a general digital camera, and a method of indirectly calculating the concentration of melanin and hemoglobin using independent component analysis has been proposed. This method targets a region of a certain RGB image, extracts characteristic vectors of melanin and hemoglobin, and calculates the concentration in a manner similar to that of Principal Component Analysis. The disadvantage of this method is that it is difficult to directly calculate the pixel unit because a group of pixels in a certain area is used as an input, and since the extracted feature vector is implemented by an optimization method, it tends to be calculated with a different value each time it is executed. The final calculation is determined in the form of an image representing the components of melanin and hemoglobin by converting it back to the RGB coordinate system without using the feature vector itself. In order to improve the disadvantages of this method, the proposed method is to calculate the component values of melanin and hemoglobin in a feature space rather than an RGB coordinate system using a feature vector, and calculate the spectral reflectance corresponding to the skin color using a general digital camera. Methods and methods of calculating detailed components constituting skin pigments such as melanin, oxidized hemoglobin, deoxidized hemoglobin, and carotenoid using spectral reflectance. The proposed method does not require special equipment such as a spectral reflectance measuring device or a multi-spectral camera, and unlike the existing method, direct calculation of the pixel unit is possible, and the same characteristics can be obtained even in repeated execution. The standard diviation of density for melanin and hemoglobin of proposed method was 15% compared to conventional and therefore gives 6 times stable.
Keywords
Skin Color; Independent Component Analysis; Melanin; Hemoglobin;
Citations & Related Records
연도 인용수 순위
  • Reference
1 C. B. Ketan and M. K. Bhuyan, "Image specific discriminative feature extraction for skin segmentation," Multimedia Tools and Applications, Vol.79, pp.18981-19004, 2020.   DOI
2 C. Cooksey, D. W. Allen, and B. K. Tsai, "Reference data set of human skin reflectance," Journal of Research of the National Institute of Standards and Technology, Vol.122, No.26, pp.1-5, 2017.   DOI
3 N. Tsumura, H. Haneishi, and Y. Miyake, "Independent-component analysis of skin color image," Journal of Optical Society of America A, Vol.16, No.9, pp.2169-2176, 1999.
4 J. Spigulis and I. Oshina, "Snapshot RGB mapping of skin melanin and hemoglobin," Journal of Biomedical Optics, Vol.20, No.5, pp.1-3, 2015.
5 S. Tanaka and N. Tsumura, "Improved analysis for skin color separation based on independent component analysis," Artificial Life and Robotics, Vol.25, Iss.1, pp.159-166, 2020.   DOI
6 T. Kwak, M. Chang, S. Lee, S. Park, and S. Park, "Comparative Study of Melanin Content in Corneocyte with Skin Color," Journal of the Society of Cosmetic Scientists of Korea, Vol.36, No.3, pp.193-198, 2010.
7 P. V. Gehler, C. Rother,A. Blake, T. Sharp, and T. Minka, "Bayesian color constancy revisited," Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2008.
8 L. Sigal, S. Sclaroff, and V. Athitsos, "Skin color-based video segmentation under time-varying illumination," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.26, No.7, pp.862-877, 2004.   DOI
9 J. Lu, J. H. Manton, E. Kazmierczak, and R. Sinclair, "Erythema detection in digital skin images," 2010 IEEE International Conference on Image Processing, Hong Kong, pp.2545-2548, 2010.
10 K. B. Shaik, P. Ganesan, V. Kalist, B. S. Sathish, and J. M. M. Jenitha, "Comparative study of skin color detection and segmentation in HSV and YCbCr color space," Procedia Computer Science, Vol.57, No.12, pp.41-48, 2015.   DOI