Multimedia Tools and Applications

, Volume 64, Issue 2, pp 227–247 | Cite as

Skin feature extraction and processing model for statistical skin age estimation

  • Young-Hwan Choi
  • Yoon-Sik Tak
  • Seungmin Rho
  • Eenjun Hwang


The convergence of information and medical technologies has resulted in the emergence and active development of the ubiquitous healthcare (U-Healthcare) industry. The U-healthcare industry provides telepathology and anytime-anywhere wellness services. The main purpose of these wellness services is to provide health information to improve the quality of life. Human skin is an organ that can be easily examined without expensive devices. In addition, there has recently been rapidly increasing interest in skin care products, resulting in a concomitant increase in their consumption. In this paper, we propose a new scheme for a self-diagnostic application that can estimate the actual age of the skin on the basis of the features on a skin image. In accordance with dermatologists’ suggestions, we examined the length, width, depth, and other cell features of skin wrinkles to evaluate skin age. Using our highly developed image processing method, we could glean detailed information from the surface of the skin. Our scheme uses the extracted information as features to train a support vector machine (SVM) and evaluates the age of a subject’s skin. Evaluation of our proposed scheme showed that it was more than 90% accurate in the analysis of the skin age of three different parts of the body: the face, neck, and hands. Therefore, we believe our model can be used as a standard or as a scale to measure the degree of damage or the aging process of the skin. This scheme is implemented into our Self-Diagnostic Total Skin Care system, and the information obtained from this system can be utilized in various areas of medicine.


Skin age Skin surface analysis SVM classification Skin feature extraction 



This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0026448) and the MKE (Ministry of Knowledge Economy), Korea, under the ITRC (Information Technology Research Center) support program supervised by the NIPA (National IT Industry Promotion Agency) (NIPA-2012- C1090-1201-0008).


  1. 1.
    Akazaka S, Nakagawa H, Kazama H, Osanai O, Kawai M, Takema Y, Imokawa G (2002) Age-related changes in skin wrinkles assessed by a novel three-dimensional morphometric analysis. Br J Dermatol 147:689–695CrossRefGoogle Scholar
  2. 2.
    Beucher S, Lantuejoul C, (1979) “Use of Watersheds watersheds in contour detection,”. In procProc. International Int’l workshop on image processing, Real-time edge and motion detection/estimationGoogle Scholar
  3. 3.
    Boyer G, Laquièze L, Le Bot A, Laquièze S, Zahouani H (2009) Dynamic indentation on human skin in vivo: ageing Ageing effects. Skin Research and Technology 15:55–67CrossRefGoogle Scholar
  4. 4.
    Choi Y, Kim K, Hwang E (2008) WASUP: A wrinkle analysis A using microscopic skin image. In Proceedings of Int’l Conference on Ubiquitous Information Technologies & ApplicationsGoogle Scholar
  5. 5.
    Christopher JC, Burges CJC (1998) A tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2:121–167CrossRefGoogle Scholar
  6. 6.
    Edwards C, Heggie R, Marks R (2003) A study of difference in surface roughness between sun-exposed and unexposed skin with age. Photodermatol Photoimmunol Photomed 19:169–174CrossRefGoogle Scholar
  7. 7.
    EmguCV: cross Cross platform .Net wrapper to the OpenCV,
  8. 8.
    Fischer TW, Wigger-Alberti W, Elsner P (1999) Direct and nondirect measurement techniques for analysis of skin surface topography. Skin Pharmacol Appl Skin Physiol 12:1–11CrossRefGoogle Scholar
  9. 9.
    Fujimura T, Haketa K, Hotta M, Kitahara T (2007) Global and systematic demonstration for the practical usage of a direct in vivo measurement system to evaluate wrinkles. Int J Cosmet Sci 29:423–436CrossRefGoogle Scholar
  10. 10.
    Ilive D, Hinnen U, Elsner P (1997) Skin roughness is negatively correlated to irritation with DMSO, but not with NaOH and SLS. Exp Dermatol 6:157–160CrossRefGoogle Scholar
  11. 11.
    John Hatzis J (2004) The wrinkle and its measurement -: A skin surface profilometric method. Micron 35(3):201–219CrossRefGoogle Scholar
  12. 12.
    Jun-Ichiro Hayashi J, Koshimizu H, Hata S. et al. (2003) “Age and Gender gender Estimation estimation Based based on Facial facial Image image Analysisanalysis,”. KES 2003,; LNAI 2774, : pp. 863–-869Google Scholar
  13. 13.
    Jun-Ichiro Hayashi J, Yasumoto M, Ito H, Koshimizu H et al (2002) Age and Gender gender Estimation estimation Based based on Wrinkle wrinkle Texture texture and Color color of Facial facial Imagesimages. Int Conf on Pattern Recognition 1:10405405–408Google Scholar
  14. 14.
    Kim K, Choi Y, Hwang E (2009) Wrinkle feature-based skin age estimation scheme. In Proceedings of Int’l Conference on Multimedia and ExpoGoogle Scholar
  15. 15.
    Lagarde JM, Rouvrais C, Black D (2005) Topography and anisotropy of the skin surface with ageing. Skin Res Technol 11:110–119CrossRefGoogle Scholar
  16. 16.
    Lagarde JM, Rouvrais C, Black D, Diridollou S, Gall Y (2001) Skin topography measurement by interference fringe projection: a A technical validation. Skin Res Technol 7:112–121CrossRefGoogle Scholar
  17. 17.
    Leveque JL, Querleux B (2003) SkinChip, a new tool for investigating the skin surface in vivo. Skin Res Technol 9:343–347CrossRefGoogle Scholar
  18. 18.
    LIBSVM – A Library for Support Vector Machine,
  19. 19.
    Nita D, Mignot J, Chuard M, Sofa M (1998) 3-D profilometer using a CCD linear image sensor: application to skin surface topography measurement. Skin Res Technol 4:121–129CrossRefGoogle Scholar
  20. 20.
    Purba MB, Kouruis-Blazos A, Wattanapenpaiboon N, Lukito W, Rothenberg E, Steen B, Wahlqvist ML (2001) Can skin wrinkling in a site that has received limited sun exposure be used as a marker of health status and biological age?, Age Ageing 30:227–234CrossRefGoogle Scholar
  21. 21.
    Tanaka H, Nakagami G, Sanada H, Sari Y, Kobayashi H, Kishi K, Konya C, Tadaka EH, Tanaka et al (2008) Quantitative evaluation of elderly skin based on digital image analysis. Skin research and technology 14:192–200CrossRefGoogle Scholar
  22. 22.
    Yaobin Zou Y, Song E, Jin R et al (2009) Age-dependent changes in skin surface assessed by a novel two-dimensional image analysis. Skin Research and Technology 15(4):399–406CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Young-Hwan Choi
    • 1
  • Yoon-Sik Tak
    • 1
  • Seungmin Rho
    • 2
  • Eenjun Hwang
    • 1
  1. 1.School of Electrical EngineeringKorea UniversitySeoulSouth Korea
  2. 2.Division of Information and CommunicationBaekseok UniversityCheonanSouth Korea

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