The week's pick
Random Articles
Reseach Article
Estimation of Skin Moisture and Elasticity from Facial Image by using Kernel Ridge Regression
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 72 - Number 3 |
| Year of Publication: 2013 |
| Authors: Motoki Sakai, Yuichi Okuyama |
10.5120/12473-8866
|
Motoki Sakai, Yuichi Okuyama . Estimation of Skin Moisture and Elasticity from Facial Image by using Kernel Ridge Regression. International Journal of Computer Applications. 72, 3 ( June 2013), 12-18. DOI=10.5120/12473-8866
Abstract
Various assessment characteristics have been used to evaluate the physiological condition of the skin, including skin moisture, elasticity, oil, and color. This often requires specific pieces of equipment such as a microscope. Although everyday evaluations may be needed to maintain skin condition, a particular piece of equipment may not be suitable for daily use. In this paper, it was proposed that a method to estimate skin moisture and elasticity from a facial image shot by a typical camera. The facial image's RGB, HSV, and YCrCb components were extracted as the explanatory variables for kernel ridge regression (KRR). In general processing, one color space is often adopted for a single purpose. In this research, some of the color components of various color spaces were selectively combined as explanatory variables for KRR. To select suitable explanatory variables, the sequential feature selection (SFS) method was applied. As a result, the correlation coefficient between the estimated and measured skin moisture values was 0. 35. These results showed that skin moisture estimation using the facial image was insufficient. In contrast, the correlation coefficient between the estimated and measured skin elasticity values was 0. 72, indicating that the skin elasticity estimation was successful.
References
- S. Akazaki, M. Zama, N. Inoue, H. Negishi and M. Kawai, "A Study for the Relationship between Subjective and Physiological Evaluation of the Facial Skin Condition in Healthy Japanese Females," J. Jpn. Cosm. Sci. Soc. , Vol. 17, No. 1, pp. 6-14, 1993
- J. McCullough, R. Garcia and B. Reece, "A clinical study of topical Pyratine 6 for improving the appearance of photodamaged skin," J. Drugs in Dermatol. ,Vol. 7 issue 2, pp. 131-135, 2008
- Y. Takemae, H. Saito and S. Ozawa, "The evaluating system of human skin surface condition by image processing," Systems, Man, and Cybernetics, 2000 IEEE International Conference on, 218-223 Vol. 1, 2000
- S. Eun-Jung, W. Young-Ah and K. Hyo-Jin, "Determination of water content in skin by using a ft near infrared spectrometer,"Archives of Pharmacal Research, Vol. 28, No. 4, 458-462, 2005
- J. Weichers and T. Barlow, "Skin moisturisation and elasticity originate from at least two different mechanisms," Int. J. Cosm. Sci. , pp. 425-435, 1999
- Y. Inoue, T. Kaneko, N. Ojima and K. Minami, "Tendency of Face Color Control by Make-up," Proceeding of the color science association of Japan, pp. 22-23, 1998
- N. Ojima, "Image Analysis of Skin Color Using Independent Component Analysis and Its Application to Melanin Pigmentation Analysis," Journal of SCCJ, Vol. 41, No. 3, pp. 159-166, 2007
- N. Tsumura, H. Haneishi and Y. Miyake, "Independent component analysis of skin color image,"J. Opt. Soc. Am. , A, Vol. 16, Issue 9, pp. 2169-2176, 1999
- D. Basak, S. Pal and D. C. Patranabis, "Support Vector Regression," Neural Information Processing, Vol. 10, No. 10, October, pp. 203-224, 2007
- V. N. Vapnik, "Statistical learning theory," In A. Gammerman, editor, Computational Learning and Probabilistic Reasoning. Wiley, 1996
- M. O. Mendez, J. Corthout, S. Van Huffel, M. Matteucci, T. Penzel, S. Certti and A. M. Bianchi, "Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis," Physiol. Means. , 31, pp. 273-289, 2010
- C. Saunders, A. Gammerman and V. Vovk, "Ridge Regression Learning Algorithm in Dual Variables," Proceedings of the 15th International Conference on Machine Learning, ICML'98, pp. 515-521, 1998
- A. Karatzoglou, T. Universität Wien, A. Smola, K. Hornik and W. Wien, "kernlab – An S4 Package for Kernel Methods in R," J. Stat. Software, Vol. 11, issue 9, 2004
Index Terms
Keywords